Repository: IBM/differential-privacy-library

Issue:
Factor of 2 in objective for DP logistic regression
Hi, I wanted to check about a factor of 2 in the implementation regarding the conversion `\Lambda = 1 / (2 n C)` as discussed in a previous PR https://github.com/IBM/differential-privacy-library/issues/10

In Corollary 11 of [CMS18] their regularization term N(f) = 1/2 ||f||^2 so their proof takes into account the 1/2 factor in the objective formulation (that is, `J = 1/n sum(loss) + 1/2 \Lambda ||f||^2` ), just like in the sklearn objective which is `J= C*sum(loss) + 1/2 w^t w` . So if this is the case then the conversion should be `\Lambda = 1 / (n C)`, and `\Lambda = alpha / n`, which implies in your code 0.5 * alpha would just be alpha
e.g. https://github.com/IBM/differential-privacy-library/blob/e9bbdb7a92659ccc5734bee81b330d85ea782925/diffprivlib/mechanisms/vector.py#L139

Thank you

Hints from issue comments:

Solution patch:
diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index 0a2379d..1a1cdcc 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -37,7 +37,7 @@ jobs:
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL
-      uses: github/codeql-action/init@v2
+      uses: github/codeql-action/init@v3
       with:
         languages: ${{ matrix.language }}
 
diff --git a/.github/workflows/general.yml b/.github/workflows/general.yml
index 6aa48da..d0bb661 100644
--- a/.github/workflows/general.yml
+++ b/.github/workflows/general.yml
@@ -19,7 +19,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
+        python-version: ['3.9', '3.10', '3.11', '3.12', '3.13']
 
     steps:
     - uses: actions/checkout@v3
diff --git a/.github/workflows/libraries.yml b/.github/workflows/libraries.yml
index 397dcc5..96325b0 100644
--- a/.github/workflows/libraries.yml
+++ b/.github/workflows/libraries.yml
@@ -20,33 +20,24 @@ jobs:
       matrix:
         include:
           - library: numpy
-            version: 1.24.4
+            version: 2.0.2
             python-version: '3.11'
           - library: numpy
-            version: 1.25.2
-            python-version: '3.11'
-          - library: numpy
-            version: 1.26.4
+            version: 2.1.3
             python-version: '3.11'
 
-          - library: scikit-learn-legacy
-            version: 1.2.2
-            python-version: '3.10'
-          - library: scikit-learn
-            version: 1.3.2
-            python-version: '3.11'
           - library: scikit-learn
             version: 1.4.2
             python-version: '3.11'
+          - library: scikit-learn
+            version: 1.5.2
+            python-version: '3.12'
 
           - library: scipy
-            version: 1.9.3
-            python-version: '3.11'
-          - library: scipy
-            version: 1.10.1
+            version: 1.13.1
             python-version: '3.11'
           - library: scipy
-            version: 1.11.4
+            version: 1.14.1
             python-version: '3.11'
 
           - library: crlibm
@@ -74,11 +65,6 @@ jobs:
       run: |
         python -m pip install scikit-learn==${{ matrix.version }}
 
-    - name: Pre-install scikit-learn (legacy)
-      if: ${{ matrix.library == 'scikit-learn-legacy' }}
-      run: |
-        python -m pip install scikit-learn==${{ matrix.version }} numpy==1.26.4
-
     - name: Pre-install scipy
       if: ${{ matrix.library == 'scipy' }}
       run: |
diff --git a/diffprivlib/mechanisms/vector.py b/diffprivlib/mechanisms/vector.py
index d59847a..c5699d4 100644
--- a/diffprivlib/mechanisms/vector.py
+++ b/diffprivlib/mechanisms/vector.py
@@ -42,7 +42,7 @@ class Vector(DPMechanism):
         The function sensitivity of the mechanism.  Must be in [0, ∞).
 
     data_sensitivity : float, default: 1.0
-        The data sensitivityof the mechanism.  Must be in [0, ∞).
+        The data sensitivity of the mechanism.  Must be in [0, ∞).
 
     dimension : int
         Function input dimension.  This dimension relates to the size of the input vector of the function being
@@ -52,18 +52,22 @@ class Vector(DPMechanism):
     alpha : float, default: 0.01
         Regularisation parameter.  Must be in (0, ∞).
 
+    n : int, default: 1
+        Size of the training dataset, required to calibrate the influence of the random vector in the objective.
+
     random_state : int or RandomState, optional
         Controls the randomness of the mechanism.  To obtain a deterministic behaviour during randomisation,
         ``random_state`` has to be fixed to an integer.
 
     """
-    def __init__(self, *, epsilon, function_sensitivity, data_sensitivity=1.0, dimension, alpha=0.01,
+    def __init__(self, *, epsilon, function_sensitivity, data_sensitivity=1.0, dimension, alpha=0.01, n=1,
                  random_state=None):
         super().__init__(epsilon=epsilon, delta=0.0, random_state=random_state)
         self.function_sensitivity, self.data_sensitivity = self._check_sensitivity(function_sensitivity,
                                                                                    data_sensitivity)
         self.dimension = self._check_dimension(dimension)
         self.alpha = self._check_alpha(alpha)
+        self.n = int(n)
 
     @classmethod
     def _check_epsilon_delta(cls, epsilon, delta):
@@ -107,6 +111,9 @@ class Vector(DPMechanism):
         self._check_sensitivity(self.function_sensitivity, self.data_sensitivity)
         self._check_dimension(self.dimension)
 
+        if self.n < 1:
+            raise ValueError(f"n must be strictly positive, got {self.n}")
+
         if not callable(value):
             raise TypeError("Value to be randomised must be a function")
 
@@ -139,13 +146,12 @@ class Vector(DPMechanism):
         """
         self._check_all(value)
 
-        epsilon_p = self.epsilon - 2 * np.log(1 + self.function_sensitivity * self.data_sensitivity /
-                                              (0.5 * self.alpha))
+        epsilon_p = self.epsilon - 2 * np.log(1 + self.function_sensitivity * self.data_sensitivity / self.alpha)
         delta = 0
 
         if epsilon_p <= 0:
-            delta = (self.function_sensitivity * self.data_sensitivity / (np.exp(self.epsilon / 4) - 1)
-                     - 0.5 * self.alpha)
+            delta = (self.function_sensitivity * self.data_sensitivity / np.expm1(self.epsilon / 4)
+                     - self.alpha) / self.n
             epsilon_p = self.epsilon / 2
 
         scale = self.data_sensitivity * 2 / epsilon_p
@@ -171,11 +177,11 @@ class Vector(DPMechanism):
             else:
                 grad = None
 
-            func += np.dot(normed_noisy_vector, input_vec)
+            func += np.dot(normed_noisy_vector, input_vec) / self.n
             func += 0.5 * delta * np.dot(input_vec, input_vec)
 
             if grad is not None:
-                grad += normed_noisy_vector + delta * input_vec
+                grad += normed_noisy_vector / self.n + delta * input_vec
 
                 return func, grad
 
diff --git a/diffprivlib/models/forest.py b/diffprivlib/models/forest.py
index d3e373b..682b419 100644
--- a/diffprivlib/models/forest.py
+++ b/diffprivlib/models/forest.py
@@ -28,6 +28,13 @@ from sklearn.tree._tree import Tree, DOUBLE, DTYPE, NODE_DTYPE  # pylint: disabl
 from sklearn.ensemble._forest import RandomForestClassifier as skRandomForestClassifier, _parallel_build_trees
 from sklearn.tree import DecisionTreeClassifier as skDecisionTreeClassifier
 
+# TODO: remove when sklearn 1.6 a min req
+try:
+    from sklearn.utils.validation import validate_data
+except ImportError:
+    from sklearn.base import BaseEstimator
+    validate_data = BaseEstimator._validate_data
+
 from diffprivlib.accountant import BudgetAccountant
 from diffprivlib.utils import PrivacyLeakWarning, check_random_state
 from diffprivlib.mechanisms import PermuteAndFlip
@@ -147,12 +154,7 @@ class RandomForestClassifier(skRandomForestClassifier, DiffprivlibMixin):  # pyl
         self.max_depth = max_depth
         self.shuffle = shuffle
         self.accountant = BudgetAccountant.load_default(accountant)
-
-        # Todo: Remove when scikit-learn v1.2 is a min requirement
-        if hasattr(self, "estimator"):
-            self.estimator = DecisionTreeClassifier()
-        else:
-            self.base_estimator = DecisionTreeClassifier()
+        self.estimator = DecisionTreeClassifier()
         self.estimator_params = ("max_depth", "epsilon", "bounds", "classes")
 
         self._warn_unused_args(unused_args)
@@ -184,7 +186,7 @@ class RandomForestClassifier(skRandomForestClassifier, DiffprivlibMixin):  # pyl
             self._warn_unused_args("sample_weight")
 
         # Validate or convert input data
-        X, y = self._validate_data(X, y, multi_output=False, dtype=DTYPE)
+        X, y = validate_data(self, X, y, multi_output=False, dtype=DTYPE)
 
         if self.bounds is None:
             warnings.warn("Bounds have not been specified and will be calculated on the data provided. This will "
@@ -258,35 +260,19 @@ class RandomForestClassifier(skRandomForestClassifier, DiffprivlibMixin):  # pyl
         # that case. However, for joblib 0.12+ we respect any
         # parallel_backend contexts set at a higher level,
         # since correctness does not rely on using threads.
-        # Todo: Remove when scikit-learn v1.1 is a min requirement
-        try:
-            trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, prefer="threads")(
-                delayed(_parallel_build_trees)(
-                    tree=t,
-                    bootstrap=False,
-                    X=X[tree_idxs == i],
-                    y=y[tree_idxs == i],
-                    sample_weight=None,
-                    tree_idx=i,
-                    n_trees=len(trees),
-                    verbose=self.verbose,
-                )
-                for i, t in enumerate(trees)
-            )
-        except TypeError:
-            trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, prefer="threads")(
-                delayed(_parallel_build_trees)(
-                    tree=t,
-                    forest=self,
-                    X=X[tree_idxs == i],
-                    y=y[tree_idxs == i],
-                    sample_weight=None,
-                    tree_idx=i,
-                    n_trees=len(trees),
-                    verbose=self.verbose,
-                )
-                for i, t in enumerate(trees)
+        trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, prefer="threads")(
+            delayed(_parallel_build_trees)(
+                tree=t,
+                bootstrap=False,
+                X=X[tree_idxs == i],
+                y=y[tree_idxs == i],
+                sample_weight=None,
+                tree_idx=i,
+                n_trees=len(trees),
+                verbose=self.verbose,
             )
+            for i, t in enumerate(trees)
+        )
 
         # Collect newly grown trees
         self.estimators_.extend(trees)
@@ -346,34 +332,18 @@ class DecisionTreeClassifier(skDecisionTreeClassifier, DiffprivlibMixin):
 
     def __init__(self, max_depth=5, *, epsilon=1, bounds=None, classes=None, random_state=None, accountant=None,
                  criterion=None, **unused_args):
-        # Todo: Remove when scikit-learn v1.0 is a min requirement
-        try:
-            super().__init__(  # pylint: disable=unexpected-keyword-arg
-                criterion=None,
-                splitter=None,
-                max_depth=max_depth,
-                min_samples_split=None,
-                min_samples_leaf=None,
-                min_weight_fraction_leaf=None,
-                max_features=None,
-                random_state=random_state,
-                max_leaf_nodes=None,
-                min_impurity_decrease=None,
-                min_impurity_split=None
-            )
-        except TypeError:
-            super().__init__(
-                criterion=None,
-                splitter=None,
-                max_depth=max_depth,
-                min_samples_split=None,
-                min_samples_leaf=None,
-                min_weight_fraction_leaf=None,
-                max_features=None,
-                random_state=random_state,
-                max_leaf_nodes=None,
-                min_impurity_decrease=None
-            )
+        super().__init__(
+            criterion=None,
+            splitter=None,
+            max_depth=max_depth,
+            min_samples_split=None,
+            min_samples_leaf=None,
+            min_weight_fraction_leaf=None,
+            max_features=None,
+            random_state=random_state,
+            max_leaf_nodes=None,
+            min_impurity_decrease=None
+        )
         self.epsilon = epsilon
         self.bounds = bounds
         self.classes = classes
@@ -415,7 +385,7 @@ class DecisionTreeClassifier(skDecisionTreeClassifier, DiffprivlibMixin):
             self._warn_unused_args("sample_weight")
 
         if check_input:
-            X, y = self._validate_data(X, y, multi_output=False)
+            X, y = validate_data(self, X, y, multi_output=False)
         self.n_outputs_ = 1
 
         if self.bounds is None:
diff --git a/diffprivlib/models/k_means.py b/diffprivlib/models/k_means.py
index 9e075bc..8ded5a4 100644
--- a/diffprivlib/models/k_means.py
+++ b/diffprivlib/models/k_means.py
@@ -23,6 +23,13 @@ import warnings
 import numpy as np
 import sklearn.cluster as sk_cluster
 
+# TODO: remove when sklearn 1.6 a min req
+try:
+    from sklearn.utils.validation import validate_data
+except ImportError:
+    from sklearn.base import BaseEstimator
+    validate_data = BaseEstimator._validate_data
+
 from diffprivlib.accountant import BudgetAccountant
 from diffprivlib.mechanisms import LaplaceBoundedDomain, GeometricFolded
 from diffprivlib.utils import PrivacyLeakWarning, check_random_state
@@ -126,7 +133,7 @@ class KMeans(sk_cluster.KMeans, DiffprivlibMixin):
 
         random_state = check_random_state(self.random_state)
 
-        X = self._validate_data(X, accept_sparse=False, dtype=[np.float64, np.float32])
+        X = validate_data(self, X, accept_sparse=False, dtype=[np.float64, np.float32])
         n_samples, n_dims = X.shape
 
         if n_samples < self.n_clusters:
diff --git a/diffprivlib/models/linear_regression.py b/diffprivlib/models/linear_regression.py
index 5178c69..72bf9fb 100644
--- a/diffprivlib/models/linear_regression.py
+++ b/diffprivlib/models/linear_regression.py
@@ -51,6 +51,13 @@ from scipy.optimize import minimize
 from sklearn.utils import check_array
 from sklearn.utils.validation import FLOAT_DTYPES
 
+# TODO: remove when sklearn 1.6 a min req
+try:
+    from sklearn.utils.validation import validate_data
+except ImportError:
+    from sklearn.base import BaseEstimator
+    validate_data = BaseEstimator._validate_data
+
 from diffprivlib.accountant import BudgetAccountant
 from diffprivlib.mechanisms import Laplace, LaplaceFolded
 from diffprivlib.tools import mean
@@ -265,7 +272,7 @@ class LinearRegression(sk_lr.LinearRegression, DiffprivlibMixin):
 
         random_state = check_random_state(self.random_state)
 
-        X, y = self._validate_data(X, y, accept_sparse=False, y_numeric=True, multi_output=True)
+        X, y = validate_data(self, X, y, accept_sparse=False, y_numeric=True, multi_output=True)
 
         if self.bounds_X is None or self.bounds_y is None:
             warnings.warn(
diff --git a/diffprivlib/models/logistic_regression.py b/diffprivlib/models/logistic_regression.py
index 097cde4..50eb7e1 100644
--- a/diffprivlib/models/logistic_regression.py
+++ b/diffprivlib/models/logistic_regression.py
@@ -53,15 +53,15 @@ from sklearn.exceptions import ConvergenceWarning
 from sklearn import linear_model
 from sklearn.utils import check_array, check_consistent_length
 from sklearn.utils.multiclass import check_classification_targets
+from sklearn.linear_model._linear_loss import LinearModelLoss
+from sklearn._loss import HalfBinomialLoss
 
-# todo: Remove when sklearn v1.1.0 is min requirement
+# TODO: remove when sklearn v1.6 a min req
 try:
-    from sklearn.linear_model._linear_loss import LinearModelLoss
-    from sklearn._loss import HalfBinomialLoss
-    SKL_LOSS_MODULE = True
-except (ModuleNotFoundError, ImportError):
-    from sklearn.linear_model._logistic import _logistic_loss_and_grad
-    SKL_LOSS_MODULE = False
+    from sklearn.utils.validation import validate_data
+except ImportError:
+    from sklearn.base import BaseEstimator
+    validate_data = BaseEstimator._validate_data
 
 from diffprivlib.accountant import BudgetAccountant
 from diffprivlib.mechanisms import Vector
@@ -221,16 +221,8 @@ class LogisticRegression(linear_model.LogisticRegression, DiffprivlibMixin):
 
         random_state = check_random_state(self.random_state)
 
-        # Todo: Remove when scikit-learn v1.2 is a min requirement
-        if not isinstance(self.C, numbers.Real) or self.C < 0:
-            raise ValueError(f"Penalty term must be positive; got (C={self.C})")
-        if not isinstance(self.max_iter, numbers.Integral) or self.max_iter < 0:
-            raise ValueError(f"Maximum number of iteration must be positive; got (max_iter={self.max_iter})")
-        if not isinstance(self.tol, numbers.Real) or self.tol < 0:
-            raise ValueError(f"Tolerance for stopping criteria must be positive; got (tol={self.tol})")
-
-        X, y = self._validate_data(X, y, accept_sparse='csr', dtype=float, order="C",
-                                   accept_large_sparse=True)
+        X, y = validate_data(self, X, y, accept_sparse='csr', dtype=float, order="C",
+                             accept_large_sparse=True)
         check_classification_targets(y)
         self.classes_ = np.unique(y)
         _, n_features = X.shape
@@ -383,11 +375,11 @@ def _logistic_regression_path(X, y, epsilon, data_norm, pos_class=None, Cs=10, f
 
     sample_weight = np.ones(X.shape[0], dtype=X.dtype)
 
-    # For doing a ovr, we need to mask the labels first.
+    # For doing an ovr, we need to mask the labels first.
     output_vec = np.zeros(n_features + int(fit_intercept), dtype=X.dtype)
     mask = (y == pos_class)
     y_bin = np.ones(y.shape, dtype=X.dtype)
-    y_bin[~mask] = 0.0 if SKL_LOSS_MODULE else -1.0
+    y_bin[~mask] = 0.0
 
     if coef is not None:
         # it must work both giving the bias term and not
@@ -397,28 +389,21 @@ def _logistic_regression_path(X, y, epsilon, data_norm, pos_class=None, Cs=10, f
         output_vec[:coef.size] = coef
 
     target = y_bin
-
-    if SKL_LOSS_MODULE:
-        func = LinearModelLoss(base_loss=HalfBinomialLoss(), fit_intercept=fit_intercept).loss_gradient
-        sw_sum = n_samples
-    else:
-        func = _logistic_loss_and_grad
-        sw_sum = 1
+    func = LinearModelLoss(base_loss=HalfBinomialLoss(), fit_intercept=fit_intercept).loss_gradient
 
     coefs = []
     n_iter = np.zeros(len(Cs), dtype=np.int32)
     for i, C in enumerate(Cs):
-        l2_reg_strength = 1.0 / (C * sw_sum)
-        vector_mech = Vector(epsilon=epsilon, dimension=n_features + int(fit_intercept), alpha=l2_reg_strength,
-                             function_sensitivity=0.25, data_sensitivity=data_norm, random_state=random_state)
+        l2_reg_strength = 1.0 / (C * n_samples)
+        vector_mech = Vector(epsilon=epsilon, dimension=n_features + int(fit_intercept), alpha=1.0 / C,
+                             function_sensitivity=0.25, data_sensitivity=data_norm, n=n_samples,
+                             random_state=random_state)
         noisy_logistic_loss = vector_mech.randomise(func)
 
-        args = (X, target, sample_weight, l2_reg_strength) if SKL_LOSS_MODULE else (X, target, l2_reg_strength,
-                                                                                    sample_weight)
-
+        args = (X, target, sample_weight, l2_reg_strength)
         iprint = [-1, 50, 1, 100, 101][np.searchsorted(np.array([0, 1, 2, 3]), verbose)]
-        output_vec, _, info = optimize.fmin_l_bfgs_b(noisy_logistic_loss, output_vec, fprime=None,
-                                                     args=args, iprint=iprint, pgtol=tol, maxiter=max_iter)
+        output_vec, _, info = optimize.fmin_l_bfgs_b(noisy_logistic_loss, output_vec, fprime=None, factr=64, args=args,
+                                                     iprint=iprint, pgtol=tol, maxiter=max_iter)
         if info["warnflag"] == 1:
             warnings.warn("lbfgs failed to converge. Increase the number of iterations.", ConvergenceWarning)
 
diff --git a/diffprivlib/models/naive_bayes.py b/diffprivlib/models/naive_bayes.py
index c7a47d5..06a39b4 100644
--- a/diffprivlib/models/naive_bayes.py
+++ b/diffprivlib/models/naive_bayes.py
@@ -24,6 +24,13 @@ import numpy as np
 import sklearn.naive_bayes as sk_nb
 from sklearn.utils.multiclass import _check_partial_fit_first_call
 
+# TODO: remove when sklearn 1.6 a min req
+try:
+    from sklearn.utils.validation import validate_data
+except ImportError:
+    from sklearn.base import BaseEstimator
+    validate_data = BaseEstimator._validate_data
+
 from diffprivlib.accountant import BudgetAccountant
 from diffprivlib.mechanisms import LaplaceBoundedDomain, GeometricTruncated, LaplaceTruncated
 from diffprivlib.utils import PrivacyLeakWarning, warn_unused_args, check_random_state
@@ -101,7 +108,7 @@ class GaussianNB(sk_nb.GaussianNB, DiffprivlibMixin):
 
         random_state = check_random_state(self.random_state)
 
-        X, y = self._validate_data(X, y)
+        X, y = validate_data(self, X, y)
 
         if self.bounds is None:
             warnings.warn("Bounds have not been specified and will be calculated on the data provided. This will "
@@ -297,9 +304,3 @@ class GaussianNB(sk_nb.GaussianNB, DiffprivlibMixin):
             i = (i - sgn) % len(unique_y)
 
         return noisy_counts
-
-    @property
-    def sigma_(self):
-        """Variance of each feature per class."""
-        # Todo: Consider removing when sklearn v1.0 is required
-        return self.var_
diff --git a/diffprivlib/models/pca.py b/diffprivlib/models/pca.py
index 3ef9f1a..d656169 100644
--- a/diffprivlib/models/pca.py
+++ b/diffprivlib/models/pca.py
@@ -200,11 +200,6 @@ class PCA(sk_pca.PCA, DiffprivlibMixin):
 
         self._warn_unused_args(unused_args)
 
-    # Todo: Remove when scikit-learn v1.2 is a min requirement
-    @property
-    def n_features_(self):
-        return self.n_features_in_
-
     def _fit_full(self, X, n_components, xp=None, is_array_api_compliant=False):
         self.accountant.check(self.epsilon, 0)
 
diff --git a/diffprivlib/models/standard_scaler.py b/diffprivlib/models/standard_scaler.py
index a018def..0de58f1 100644
--- a/diffprivlib/models/standard_scaler.py
+++ b/diffprivlib/models/standard_scaler.py
@@ -49,6 +49,13 @@ import numpy as np
 import sklearn.preprocessing as sk_pp
 from sklearn.preprocessing._data import _handle_zeros_in_scale
 
+# TODO: remove when sklearn 1.6 a min req
+try:
+    from sklearn.utils.validation import validate_data
+except ImportError:
+    from sklearn.base import BaseEstimator
+    validate_data = BaseEstimator._validate_data
+
 from diffprivlib.accountant import BudgetAccountant
 from diffprivlib.utils import PrivacyLeakWarning, check_random_state
 from diffprivlib.tools import nanvar, nanmean
@@ -210,8 +217,7 @@ class StandardScaler(sk_pp.StandardScaler, DiffprivlibMixin):
 
         epsilon_0 = self.epsilon / 2 if self.with_std else self.epsilon
 
-        X = self._validate_data(X, accept_sparse=False, copy=self.copy, estimator=self, dtype=float,
-                                force_all_finite='allow-nan')
+        X = validate_data(self, X, accept_sparse=False, copy=self.copy, estimator=self, dtype=float)
 
         if self.bounds is None:
             warnings.warn("Bounds parameter hasn't been specified, so falling back to determining bounds from the "
diff --git a/notebooks/logistic_regression.ipynb b/notebooks/logistic_regression.ipynb
index 67600b4..68b565f 100644
--- a/notebooks/logistic_regression.ipynb
+++ b/notebooks/logistic_regression.ipynb
@@ -31,10 +31,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X_train = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
+    "X_train = np.genfromtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
     "                        usecols=(0, 4, 10, 11, 12), delimiter=\",\")\n",
     "\n",
-    "y_train = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
+    "y_train = np.genfromtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
     "                        usecols=14, dtype=str, delimiter=\",\")"
    ]
   },
@@ -71,11 +71,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X_test = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
-    "                        usecols=(0, 4, 10, 11, 12), delimiter=\",\", skiprows=1)\n",
+    "X_test = np.genfromtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
+    "                        usecols=(0, 4, 10, 11, 12), delimiter=\",\", skip_header=1)\n",
+    "\n",
+    "y_test = np.genfromtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
+    "                        usecols=14, dtype=str, delimiter=\",\", skip_header=1)\n",
     "\n",
-    "y_test = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
-    "                        usecols=14, dtype=str, delimiter=\",\", skiprows=1)\n",
     "# Must trim trailing period \".\" from label\n",
     "y_test = np.array([a[:-1] for a in y_test])"
    ]
@@ -117,17 +118,430 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=150)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=150)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
-       "LogisticRegression()"
+       "LogisticRegression(max_iter=150)"
       ]
      },
      "execution_count": 6,
@@ -136,7 +550,7 @@
     }
    ],
    "source": [
-    "clf = LogisticRegression(solver=\"lbfgs\")\n",
+    "clf = LogisticRegression(solver=\"lbfgs\", max_iter=150)\n",
     "clf.fit(X_train, y_train)"
    ]
   },
@@ -149,7 +563,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Non-private test accuracy: 81.25%\n"
+      "Non-private test accuracy: 81.28%\n"
      ]
     }
    ],
@@ -179,28 +593,441 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ".../site-packages/diffprivlib/models/logistic_regression.py:233: PrivacyLeakWarning: Data norm has not been specified and will be calculated on the data provided.  This will result in additional privacy leakage. To ensure differential privacy and no additional privacy leakage, specify `data_norm` at initialisation.\n",
+      "venv3.12/lib/python3.12/site-packages/diffprivlib/models/logistic_regression.py:231: PrivacyLeakWarning: Data norm has not been specified and will be calculated on the data provided.  This will result in additional privacy leakage. To ensure differential privacy and no additional privacy leakage, specify `data_norm` at initialisation.\n",
       "  warnings.warn(\"Data norm has not been specified and will be calculated on the data provided.  This will \"\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(accountant=BudgetAccountant(spent_budget=[(1.0, 0)]),\n",
-       "                   data_norm=99999.04562544584, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(accountant=BudgetAccountant(spent_budget=[(1.0, 0)]),\n",
-       "                   data_norm=99999.04562544584, random_state=0)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(accountant=BudgetAccountant(spent_budget=[(1.0, 0)]),\n",
+       "                   data_norm=np.float64(99999.04562544584), random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(accountant=BudgetAccountant(spent_budget=[(1.0, 0)]),\n",
+       "                   data_norm=np.float64(99999.04562544584), random_state=0)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "LogisticRegression(accountant=BudgetAccountant(spent_budget=[(1.0, 0)]),\n",
-       "                   data_norm=99999.04562544584, random_state=0)"
+       "                   data_norm=np.float64(99999.04562544584), random_state=0)"
       ]
      },
      "execution_count": 8,
@@ -241,20 +1068,433 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(accountant=BudgetAccountant(spent_budget=[(1.0, 0), (inf, 0)]),\n",
-       "                   data_norm=100000.0, epsilon=inf)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(accountant=BudgetAccountant(spent_budget=[(1.0, 0), (inf, 0)]),\n",
-       "                   data_norm=100000.0, epsilon=inf)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(accountant=BudgetAccountant(spent_budget=[(1.0, 0), (inf, 0)]),\n",
+       "                   data_norm=100000.0, epsilon=inf, max_iter=150)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(accountant=BudgetAccountant(spent_budget=[(1.0, 0), (inf, 0)]),\n",
+       "                   data_norm=100000.0, epsilon=inf, max_iter=150)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "LogisticRegression(accountant=BudgetAccountant(spent_budget=[(1.0, 0), (inf, 0)]),\n",
-       "                   data_norm=100000.0, epsilon=inf)"
+       "                   data_norm=100000.0, epsilon=inf, max_iter=150)"
       ]
      },
      "execution_count": 10,
@@ -263,7 +1503,7 @@
     }
    ],
    "source": [
-    "dp_clf = dp.LogisticRegression(epsilon=float(\"inf\"), data_norm=1e5)\n",
+    "dp_clf = dp.LogisticRegression(epsilon=float(\"inf\"), data_norm=1e5, max_iter=150)\n",
     "dp_clf.fit(X_train, y_train)"
    ]
   },
@@ -310,9 +1550,10 @@
     "accuracy = []\n",
     "epsilons = np.logspace(-3, 1, 500)\n",
     "random_state = check_random_state(42)\n",
+    "n = X_train.shape[0]  # Increased regularisation improves performance in this example\n",
     "\n",
     "for eps in epsilons:\n",
-    "    dp_clf = dp.LogisticRegression(epsilon=eps, data_norm=100, random_state=random_state)\n",
+    "    dp_clf = dp.LogisticRegression(epsilon=eps, data_norm=2, random_state=random_state, C=1/n)\n",
     "    dp_clf.fit(X_train, y_train)\n",
     "    accuracy.append(dp_clf.score(X_test, y_test))"
    ]
@@ -321,50 +1562,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's save the results using `pickle` so we can reproduce the plot easily in the future."
+    "Let's plot the results using `matplotlib`. Because of the norm-clipping applied to the dataset before training (`data_norm=2`), the accuracy plateaus without reaching the non-private baseline."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pickle\n",
-    "\n",
-    "pickle.dump((epsilons, baseline, accuracy), open(\"lr_accuracy_500.p\", \"wb\" ) )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's plot the results using `matplotlib`. The discontinuty observed near `epsilon = 10` is an artifact of the model. Because of the norm-clipping applied to the dataset before training (`data_norm=100`), the accuracy plateaus without reaching the non-private baseline."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "import pickle\n",
-    "\n",
-    "epsilons, baseline, accuracy = pickle.load(open(\"lr_accuracy_500.p\", \"rb\"))\n",
     "\n",
     "plt.semilogx(epsilons, accuracy, label=\"Differentially private\")\n",
     "plt.plot(epsilons, np.ones_like(epsilons) * baseline, dashes=[2,2], label=\"Non-private\")\n",
@@ -394,9 +1612,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/notebooks/pipeline.ipynb b/notebooks/pipeline.ipynb
index 8430ff6..78014eb 100644
--- a/notebooks/pipeline.ipynb
+++ b/notebooks/pipeline.ipynb
@@ -33,9 +33,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X_train = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
+    "X_train = np.genfromtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
     "                        usecols=(0, 4, 10, 11, 12), delimiter=\",\")\n",
-    "y_train = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
+    "y_train = np.genfromtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
     "                        usecols=14, dtype=str, delimiter=\",\")"
    ]
   },
@@ -45,11 +45,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X_test = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
-    "                        usecols=(0, 4, 10, 11, 12), delimiter=\",\", skiprows=1)\n",
+    "X_test = np.genfromtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
+    "                       usecols=(0, 4, 10, 11, 12), delimiter=\",\", skip_header=1)\n",
     "\n",
-    "y_test = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
-    "                        usecols=14, dtype=str, delimiter=\",\", skiprows=1)\n",
+    "y_test = np.genfromtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
+    "                       usecols=14, dtype=str, delimiter=\",\", skip_header=1)\n",
     "# Must trim trailing period \".\" from label\n",
     "y_test = np.array([a[:-1] for a in y_test])"
    ]
@@ -97,7 +97,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Non-private test accuracy: 80.30%\n"
+      "Non-private test accuracy: 80.31%\n"
      ]
     }
    ],
@@ -124,26 +124,26 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Differentially private pipeline accuracy (epsilon=3): 79.34%\n"
+      "Differentially private pipeline accuracy (epsilon=3): 78.93%\n"
      ]
     }
    ],
    "source": [
     "from diffprivlib.utils import check_random_state\n",
+    "\n",
     "random_state = check_random_state(42)\n",
+    "n = X_train.shape[0]\n",
     "\n",
     "dp_pipe = Pipeline([\n",
     "    ('scaler', models.StandardScaler(bounds=([17, 1, 0, 0, 1], [90, 160, 10000, 4356, 99]), random_state=random_state)),\n",
-    "    ('pca', models.PCA(2, data_norm=5, centered=True, random_state=random_state)),\n",
-    "    ('lr', models.LogisticRegression(data_norm=5, random_state=random_state))\n",
+    "    ('pca', models.PCA(2, data_norm=4, centered=True, random_state=random_state)),\n",
+    "    ('lr', models.LogisticRegression(data_norm=1, random_state=random_state, C=10/n))\n",
     "])\n",
     "\n",
     "dp_pipe.fit(X_train, y_train)\n",
@@ -174,24 +174,6 @@
     "    pipe_accuracy.append(dp_pipe.score(X_test, y_test))"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's save the results so they can be used later."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pickle\n",
-    "\n",
-    "pickle.dump((epsilons, baseline, pipe_accuracy), open(\"pipeline_accuracy_500.p\", \"wb\" ) )"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -203,32 +185,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can now plot the results, showing that non-private accuracy is matched from approximately `epsilon = 0.1`."
+    "We can now plot the results, showing that accuracy approaches the non-private baseline from approximately `epsilon = 0.01`, albeit with some residual noise in the outcome as a result of differential privacy."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "import pickle\n",
-    "\n",
-    "epsilons, baseline, pipe_accuracy = pickle.load(open(\"pipeline_accuracy_500.p\", \"rb\"))\n",
     "\n",
     "plt.semilogx(epsilons, pipe_accuracy, label=\"Differentially private pipeline\", zorder=10)\n",
     "plt.plot(epsilons, np.ones_like(epsilons) * baseline, dashes=[2,2], label=\"Non-private pipeline\", zorder=5)\n",
@@ -258,9 +235,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/setup.py b/setup.py
index 34ec167..a481e9c 100644
--- a/setup.py
+++ b/setup.py
@@ -21,9 +21,9 @@ def get_version(file_path):
 
 
 install_requires = [
-    'numpy >= 1.21.6',
-    'scikit-learn >= 0.24.2',
-    'scipy >= 1.7.3',
+    'numpy >= 2.0.0',
+    'scikit-learn >= 1.4.0',
+    'scipy >= 1.13.0',
     'joblib >= 0.16.0',
     'setuptools >= 49.0.0',
 ]


Test patch:
diff --git a/tests/models/test_GaussianNB.py b/tests/models/test_GaussianNB.py
index fd15db9..0eb3fd4 100644
--- a/tests/models/test_GaussianNB.py
+++ b/tests/models/test_GaussianNB.py
@@ -93,12 +93,7 @@ class TestGaussianNB(TestCase):
         for clf in [clf_dp, clf_non_private]:
             clf.fit(x_train, y_train)
 
-        # Todo: remove try...except when sklearn v1.0 is required
-        try:
-            nonprivate_var = clf_non_private.var_
-        except AttributeError:
-            nonprivate_var = clf_non_private.sigma_
-
+        nonprivate_var = clf_non_private.var_
         theta_diff = (clf_dp.theta_ - clf_non_private.theta_) ** 2
         self.assertGreater(theta_diff.sum(), 0)
 
@@ -189,7 +184,7 @@ class TestGaussianNB(TestCase):
     def test_sigma(self):
         clf = GaussianNB(epsilon=1, bounds=(-2, 2))
         clf.fit(X, y)
-        self.assertIsInstance(clf.sigma_, np.ndarray)
+        self.assertIsInstance(clf.var_, np.ndarray)
 
     def test_random_state(self):
         clf0 = GaussianNB(epsilon=1, bounds=(-2, 2), random_state=0)