Repository: scikit-learn/scikit-learn
File: examples/release_highlights/plot_release_highlights_1_8_0.py (Python)

Code before:

from sklearn.datasets import make_regression
from sklearn.linear_model import ElasticNetCV

X, y = make_regression(n_features=5000)
model = ElasticNetCV()
tic = time()
model.fit(X, y)
toc = time()
print(f"Fitting ElasticNetCV took {toc - tic:.3} seconds.")

# %%
# API changes in logistic regression
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# After the deprecation (version 1.5) and removal (now in 1.8) of the `multi_class`
# parameter in `LogisticRegression` and `LogisticRegressionCV`, the bookkeeping goes
# on. The goal for `LogisticRegressionCV` is to fix types and shapes of a few fitted
# attributes: `C_`, `l1_ratio_`, `coefs_paths_`, `scores_`, `n_iter_`. To ease the
# transition, you can choose between new and old types and shapes with the new and
# temporary parameter `use_legacy_attributes`.

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegressionCV

X, y = make_classification(n_classes=3, n_informative=4)
model = LogisticRegressionCV(
    use_legacy_attributes=True, l1_ratios=(0,), solver="newton-cholesky"
).fit(X, y)
model.C_  # ndarray of shape (3,), 3 times the same value
model = LogisticRegressionCV(
    use_legacy_attributes=False, l1_ratios=(0,), solver="newton-cholesky"
).fit(X, y)
model.C_  # single float

# %%
# A further deprecation is going on for the `penalty` parameter in both
# `LogisticRegression` and `LogisticRegressionCV`. It is redundant because `C` together
# with `l1_ratio` for `LogisticRegression` and `l1_ratios` for `LogisticRegressionCV`
# contains the same information. Removing `penalty` can ease specifying grid search
# parameter spaces and provides a tidier API.

# %%
# HTML representation of estimators
# ---------------------------------
# TODO

# %%
# DecisionTreeRegressor with MAE
# ------------------------------
# TODO


Review comment (performance):
```suggestion
# and `Ecosystem compatibility tracking <https://py-free-threading.github.io/tracking/>`_.
#
# The long term goal of free-threaded Python is to more efficiently leverage
# multi-core CPUs by using thread workers instead of subprocess workers for parallel
# computation when passing `n_jobs>1` in functions or estimators.
# Efficiency gains are expected by removing the need for inter-process communication.
# Note however that process-based parallelism is still the default joblib backend at
# the time of writing. You can call `joblib.parallel_config(backend="threading")` to
# change the default backend to "threading". Be aware that properly testing that
# everything is running smoothly when doing so is still an ongoing effort and that
# there are open issues to fix before considering making this the default.
```

Code after:
#   scikit-learn script and check that nothing breaks unexpectedly.
#   Note that CPython 3.14 (rather than 3.13) is strongly advised because a
#   number of free-threaded bugs have been fixed since CPython 3.13.
# - if you use some estimators with a `n_jobs` parameter, try changing the
#   default backend to threading with `joblib.parallel_config` as in the
#   snippet below. This could potentially speed-up your code because the
#   default joblib backend is process-based and incurs more overhead than
#   threads.
#
#   .. code-block:: python
#
#       grid_search = GridSearchCV(clf, param_grid=param_grid, n_jobs=4)
#       with joblib.parallel_config(backend="threading"):
#           grid_search.fit(X, y)
#
# - don't hesitate to report any issue or unexpected performance behaviour by
#   opening a `GitHub issue <https://github.com/scikit-learn/scikit-learn/issues/new/choose>`_!
#
# Free-threaded (also known as nogil) CPython is a version of CPython that aims
# to enable efficient multi-threaded use cases by removing the Global
# Interpreter Lock (GIL).
#
# For more details about free-threaded CPython see `py-free-threading doc
# <https://py-free-threading.github.io>`_, in particular `how to install a
# free-threaded CPython <https://py-free-threading.github.io/installing-cpython/>`_
# and `Ecosystem compatibility tracking <https://py-free-threading.github.io/tracking/>`_.
#
# In scikit-learn, one hope with free-threaded Python is to more efficiently
# leverage multi-core CPUs by using thread workers instead of subprocess
# workers for parallel computation when passing `n_jobs>1` in functions or
# estimators. Efficiency gains are expected by removing the need for
# inter-process communication. Be aware that switching the default joblib
# backend and testing that everything works well with free-threaded Python is an
# ongoing long-term effort.

# %%
# Temperature scaling in `CalibratedClassifierCV`
# -----------------------------------------------
# Probability calibration of classifiers with temperature scaling is available in
# :class:`calibration.CalibratedClassifierCV` by setting `method="temperature"`.
# This method is particularly well suited for multiclass problems because it provides
# (better) calibrated probabilities with a single free parameter. This is in
# contrast to all the other available calibrations methods
# which use a "One-vs-Rest" scheme that adds more parameters for each class.

from sklearn.calibration import CalibratedClassifierCV
from sklearn.datasets import make_classification
from sklearn.naive_bayes import GaussianNB

X, y = make_classification(n_classes=3, n_informative=8, random_state=42)
clf = GaussianNB().fit(X, y)