RANSAC (RANdom SAmple Consensus) algorithm.
RANSAC is an iterative algorithm for the robust estimation of parameters from a subset of inliers from the complete data set.
Read more in the User Guide.
Base estimator object which implements the following methods:
If base_estimator is None, then LinearRegression is used for target values of dtype float.
Note that the current implementation only supports regression estimators.
Minimum number of samples chosen randomly from original data. Treated as an absolute number of samples for min_samples >= 1, treated as a relative number ceil(min_samples * X.shape[0]) for min_samples < 1. This is typically chosen as the minimal number of samples necessary to estimate the given base_estimator. By default a sklearn.linear_model.LinearRegression() estimator is assumed and min_samples is chosen as X.shape[1] + 1.
Maximum residual for a data sample to be classified as an inlier. By default the threshold is chosen as the MAD (median absolute deviation) of the target values y.
This function is called with the randomly selected data before the model is fitted to it: is_data_valid(X, y). If its return value is False the current randomly chosen sub-sample is skipped.
This function is called with the estimated model and the randomly selected data: is_model_valid(model, X, y). If its return value is False the current randomly chosen sub-sample is skipped. Rejecting samples with this function is computationally costlier than with is_data_valid. is_model_valid should therefore only be used if the estimated model is needed for making the rejection decision.
Maximum number of iterations for random sample selection.
Maximum number of iterations that can be skipped due to finding zero inliers or invalid data defined by is_data_valid or invalid models defined by is_model_valid.
New in version 0.19.
Stop iteration if at least this number of inliers are found.
Stop iteration if score is greater equal than this threshold.
RANSAC iteration stops if at least one outlier-free set of the training data is sampled in RANSAC. This requires to generate at least N samples (iterations):
where the probability (confidence) is typically set to high value such as 0.99 (the default) and e is the current fraction of inliers w.r.t. the total number of samples.
String inputs, “absolute_loss” and “squared_loss” are supported which find the absolute loss and squared loss per sample respectively.
If loss is a callable, then it should be a function that takes two arrays as inputs, the true and predicted value and returns a 1-D array with the i-th value of the array corresponding to the loss on X[i].
If the loss on a sample is greater than the residual_threshold, then this sample is classified as an outlier.
New in version 0.18.
The generator used to initialize the centers. Pass an int for reproducible output across multiple function calls. See Glossary.
Best fitted model (copy of the base_estimator object).
Number of random selection trials until one of the stop criteria is met. It is always <= max_trials.
Boolean mask of inliers classified as True.
Number of iterations skipped due to finding zero inliers.
New in version 0.19.
Number of iterations skipped due to invalid data defined by is_data_valid.
New in version 0.19.
Number of iterations skipped due to an invalid model defined by is_model_valid.
New in version 0.19.
https://en.wikipedia.org/wiki/RANSAC
https://www.sri.com/sites/default/files/publications/ransac-publication.pdf
http://www.bmva.org/bmvc/2009/Papers/Paper355/Paper355.pdf
fit(X, y[, sample_weight])
Fit estimator using RANSAC algorithm.
get_params([deep])
Get parameters for this estimator.
predict(X)
Predict using the estimated model.
score(X, y)
Returns the score of the prediction.
set_params(**params)
Set the parameters of this estimator.
Fit estimator using RANSAC algorithm.
Training data.
Target values.
Individual weights for each sample raises error if sample_weight is passed and base_estimator fit method does not support it.
New in version 0.18.
If no valid consensus set could be found. This occurs if is_data_valid and is_model_valid return False for all max_trials randomly chosen sub-samples.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Predict using the estimated model.
This is a wrapper for estimator_.predict(X).
Returns predicted values.
Returns the score of the prediction.
This is a wrapper for estimator_.score(X, y).
Training data.
Target values.
Score of the prediction.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Estimator parameters.
Estimator instance.
Robust linear model estimation using RANSAC
Theil-Sen Regression
Robust linear estimator fitting


Fit estimator using RANSAC algorithm.
Training data.
Target values.
Individual weights for each sample raises error if sample_weight is passed and base_estimator fit method does not support it.
New in version 0.18.
If no valid consensus set could be found. This occurs if is_data_valid and is_model_valid return False for all max_trials randomly chosen sub-samples.


Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.


Predict using the estimated model.
This is a wrapper for estimator_.predict(X).
Returns predicted values.


Returns the score of the prediction.
This is a wrapper for estimator_.score(X, y).
Training data.
Target values.
Score of the prediction.


Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Estimator parameters.
Estimator instance.


Linear least squares with l2 regularization.
Minimizes the objective function:
This model solves a regression model where the loss function is the linear least squares function and regularization is given by the l2-norm. Also known as Ridge Regression or Tikhonov regularization. This estimator has built-in support for multi-variate regression (i.e., when y is a 2d-array of shape (n_samples, n_targets)).
Read more in the User Guide.
Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to 1 / (2C) in other linear models such as LogisticRegression or LinearSVC. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number.
Whether to fit the intercept for this model. If set to false, no intercept will be used in calculations (i.e. X and y are expected to be centered).
This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False.
If True, X will be copied; else, it may be overwritten.
Maximum number of iterations for conjugate gradient solver. For ‘sparse_cg’ and ‘lsqr’ solvers, the default value is determined by scipy.sparse.linalg. For ‘sag’ solver, the default value is 1000.
Precision of the solution.
Solver to use in the computational routines:
All last five solvers support both dense and sparse data. However, only ‘sag’ and ‘sparse_cg’ supports sparse input when fit_intercept is True.
New in version 0.17: Stochastic Average Gradient descent solver.
New in version 0.19: SAGA solver.
Used when solver == ‘sag’ or ‘saga’ to shuffle the data. See Glossary for details.
New in version 0.17: random_state to support Stochastic Average Gradient.
Weight vector(s).
Independent term in decision function. Set to 0.0 if fit_intercept = False.
Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return None.
New in version 0.17.
See also
Ridge classifier.
Ridge regression with built-in cross validation.
Kernel ridge regression combines ridge regression with the kernel trick.
fit(X, y[, sample_weight])
Fit Ridge regression model.
get_params([deep])
Get parameters for this estimator.
predict(X)
Predict using the linear model.
score(X, y[, sample_weight])
Return the coefficient of determination \(R^2\) of the prediction.
set_params(**params)
Set the parameters of this estimator.
Fit Ridge regression model.
Training data
Target values
Individual weights for each sample. If given a float, every sample will have the same weight.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Predict using the linear model.
Samples.
Returns predicted values.
Return the coefficient of determination \(R^2\) of the prediction.
The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
True values for X.
Sample weights.
\(R^2\) of self.predict(X) wrt. y.
The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Estimator parameters.
Estimator instance.


Linear least squares with l2 regularization.
Minimizes the objective function:
This model solves a regression model where the loss function is the linear least squares function and regularization is given by the l2-norm. Also known as Ridge Regression or Tikhonov regularization. This estimator has built-in support for multi-variate regression (i.e., when y is a 2d-array of shape (n_samples, n_targets)).
Read more in the User Guide.
Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to 1 / (2C) in other linear models such as LogisticRegression or LinearSVC. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number.
Whether to fit the intercept for this model. If set to false, no intercept will be used in calculations (i.e. X and y are expected to be centered).
This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False.
If True, X will be copied; else, it may be overwritten.
Maximum number of iterations for conjugate gradient solver. For ‘sparse_cg’ and ‘lsqr’ solvers, the default value is determined by scipy.sparse.linalg. For ‘sag’ solver, the default value is 1000.
Precision of the solution.
Solver to use in the computational routines:
All last five solvers support both dense and sparse data. However, only ‘sag’ and ‘sparse_cg’ supports sparse input when fit_intercept is True.
New in version 0.17: Stochastic Average Gradient descent solver.
New in version 0.19: SAGA solver.
Used when solver == ‘sag’ or ‘saga’ to shuffle the data. See Glossary for details.
New in version 0.17: random_state to support Stochastic Average Gradient.
Weight vector(s).
Independent term in decision function. Set to 0.0 if fit_intercept = False.
Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return None.
New in version 0.17.
See also
Ridge classifier.
Ridge regression with built-in cross validation.
Kernel ridge regression combines ridge regression with the kernel trick.
fit(X, y[, sample_weight])
Fit Ridge regression model.
get_params([deep])
Get parameters for this estimator.
predict(X)
Predict using the linear model.
score(X, y[, sample_weight])
Return the coefficient of determination \(R^2\) of the prediction.
set_params(**params)
Set the parameters of this estimator.
Fit Ridge regression model.
Training data
Target values
Individual weights for each sample. If given a float, every sample will have the same weight.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Predict using the linear model.
Samples.
Returns predicted values.
Return the coefficient of determination \(R^2\) of the prediction.
The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
True values for X.
Sample weights.
\(R^2\) of self.predict(X) wrt. y.
The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Estimator parameters.
Estimator instance.
Compressive sensing: tomography reconstruction with L1 prior (Lasso)
Prediction Latency
Plot Ridge coefficients as a function of the regularization
Ordinary Least Squares and Ridge Regression Variance
Plot Ridge coefficients as a function of the L2 regularization
Polynomial interpolation
HuberRegressor vs Ridge on dataset with strong outliers
Poisson regression and non-normal loss
Common pitfalls in interpretation of coefficients of linear models


Fit Ridge regression model.
Training data
Target values
Individual weights for each sample. If given a float, every sample will have the same weight.


Predict using the linear model.
Samples.
Returns predicted values.


Return the coefficient of determination \(R^2\) of the prediction.
The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
True values for X.
Sample weights.
\(R^2\) of self.predict(X) wrt. y.
The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).


Classifier using Ridge regression.
This classifier first converts the target values into {-1, 1} and then treats the problem as a regression task (multi-output regression in the multiclass case).
Read more in the User Guide.
Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to 1 / (2C) in other linear models such as LogisticRegression or LinearSVC.
Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False.
If True, X will be copied; else, it may be overwritten.
Maximum number of iterations for conjugate gradient solver. The default value is determined by scipy.sparse.linalg.
Precision of the solution.
Weights associated with classes in the form {class_label: weight}. If not given, all classes are supposed to have weight one.
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).
Solver to use in the computational routines:
‘sag’ uses a Stochastic Average Gradient descent, and ‘saga’ uses its unbiased and more flexible version named SAGA. Both methods use an iterative procedure, and are often faster than other solvers when both n_samples and n_features are large. Note that ‘sag’ and ‘saga’ fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing.
New in version 0.17: Stochastic Average Gradient descent solver.
New in version 0.19: SAGA solver.
Used when solver == ‘sag’ or ‘saga’ to shuffle the data. See Glossary for details.
Coefficient of the features in the decision function.
coef_ is of shape (1, n_features) when the given problem is binary.
Independent term in decision function. Set to 0.0 if fit_intercept = False.
Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return None.
The classes labels.
See also
Ridge regression.
Ridge classifier with built-in cross validation.
For multi-class classification, n_class classifiers are trained in a one-versus-all approach. Concretely, this is implemented by taking advantage of the multi-variate response support in Ridge.
decision_function(X)
Predict confidence scores for samples.
fit(X, y[, sample_weight])
Fit Ridge classifier model.
get_params([deep])
Get parameters for this estimator.
predict(X)
Predict class labels for samples in X.
score(X, y[, sample_weight])
Return the mean accuracy on the given test data and labels.
set_params(**params)
Set the parameters of this estimator.
Predict confidence scores for samples.
The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.
Samples.
Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.
Fit Ridge classifier model.
Training data.
Target values.
Individual weights for each sample. If given a float, every sample will have the same weight.
New in version 0.17: sample_weight support to Classifier.
Instance of the estimator.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Predict class labels for samples in X.
Samples.
Predicted class label per sample.
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Test samples.
True labels for X.
Sample weights.
Mean accuracy of self.predict(X) wrt. y.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Estimator parameters.
Estimator instance.


Classifier using Ridge regression.
This classifier first converts the target values into {-1, 1} and then treats the problem as a regression task (multi-output regression in the multiclass case).
Read more in the User Guide.
Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to 1 / (2C) in other linear models such as LogisticRegression or LinearSVC.
Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False.
If True, X will be copied; else, it may be overwritten.
Maximum number of iterations for conjugate gradient solver. The default value is determined by scipy.sparse.linalg.
Precision of the solution.
Weights associated with classes in the form {class_label: weight}. If not given, all classes are supposed to have weight one.
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).
Solver to use in the computational routines:
‘sag’ uses a Stochastic Average Gradient descent, and ‘saga’ uses its unbiased and more flexible version named SAGA. Both methods use an iterative procedure, and are often faster than other solvers when both n_samples and n_features are large. Note that ‘sag’ and ‘saga’ fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing.
New in version 0.17: Stochastic Average Gradient descent solver.
New in version 0.19: SAGA solver.
Used when solver == ‘sag’ or ‘saga’ to shuffle the data. See Glossary for details.
Coefficient of the features in the decision function.
coef_ is of shape (1, n_features) when the given problem is binary.
Independent term in decision function. Set to 0.0 if fit_intercept = False.
Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return None.
The classes labels.
See also
Ridge regression.
Ridge classifier with built-in cross validation.
For multi-class classification, n_class classifiers are trained in a one-versus-all approach. Concretely, this is implemented by taking advantage of the multi-variate response support in Ridge.
decision_function(X)
Predict confidence scores for samples.
fit(X, y[, sample_weight])
Fit Ridge classifier model.
get_params([deep])
Get parameters for this estimator.
predict(X)
Predict class labels for samples in X.
score(X, y[, sample_weight])
Return the mean accuracy on the given test data and labels.
set_params(**params)
Set the parameters of this estimator.
Predict confidence scores for samples.
The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.
Samples.
Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.
Fit Ridge classifier model.
Training data.
Target values.
Individual weights for each sample. If given a float, every sample will have the same weight.
New in version 0.17: sample_weight support to Classifier.
Instance of the estimator.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Predict class labels for samples in X.
Samples.
Predicted class label per sample.
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Test samples.
True labels for X.
Sample weights.
Mean accuracy of self.predict(X) wrt. y.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Estimator parameters.
Estimator instance.
Classification of text documents using sparse features


Predict confidence scores for samples.
The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.
Samples.
Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.


Fit Ridge classifier model.
Training data.
Target values.
Individual weights for each sample. If given a float, every sample will have the same weight.
New in version 0.17: sample_weight support to Classifier.
Instance of the estimator.


Predict class labels for samples in X.
Samples.
Predicted class label per sample.


Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Test samples.
True labels for X.
Sample weights.
Mean accuracy of self.predict(X) wrt. y.


Ridge classifier with built-in cross-validation.
See glossary entry for cross-validation estimator.
By default, it performs Leave-One-Out Cross-Validation. Currently, only the n_features > n_samples case is handled efficiently.
Read more in the User Guide.
Array of alpha values to try. Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to 1 / (2C) in other linear models such as LogisticRegression or LinearSVC.
Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).
This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False.
A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y).
Determines the cross-validation splitting strategy. Possible inputs for cv are:
Refer User Guide for the various cross-validation strategies that can be used here.
Weights associated with classes in the form {class_label: weight}. If not given, all classes are supposed to have weight one.
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))
Flag indicating if the cross-validation values corresponding to each alpha should be stored in the cv_values_ attribute (see below). This flag is only compatible with cv=None (i.e. using Leave-One-Out Cross-Validation).
Cross-validation values for each alpha (if store_cv_values=True and cv=None). After fit() has been called, this attribute will contain the mean squared errors (by default) or the values of the {loss,score}_func function (if provided in the constructor). This attribute exists only when store_cv_values is True.
Coefficient of the features in the decision function.
coef_ is of shape (1, n_features) when the given problem is binary.
Independent term in decision function. Set to 0.0 if fit_intercept = False.
Estimated regularization parameter.
Score of base estimator with best alpha.
New in version 0.23.
The classes labels.
See also
Ridge regression.
Ridge classifier.
Ridge regression with built-in cross validation.
For multi-class classification, n_class classifiers are trained in a one-versus-all approach. Concretely, this is implemented by taking advantage of the multi-variate response support in Ridge.
decision_function(X)
Predict confidence scores for samples.
fit(X, y[, sample_weight])
Fit Ridge classifier with cv.
get_params([deep])
Get parameters for this estimator.
predict(X)
Predict class labels for samples in X.
score(X, y[, sample_weight])
Return the mean accuracy on the given test data and labels.
set_params(**params)
Set the parameters of this estimator.
Predict confidence scores for samples.
The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.
Samples.
Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.
Fit Ridge classifier with cv.
Training vectors, where n_samples is the number of samples and n_features is the number of features. When using GCV, will be cast to float64 if necessary.
Target values. Will be cast to X’s dtype if necessary.
Individual weights for each sample. If given a float, every sample will have the same weight.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Predict class labels for samples in X.
Samples.
Predicted class label per sample.
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Test samples.
True labels for X.
Sample weights.
Mean accuracy of self.predict(X) wrt. y.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Estimator parameters.
Estimator instance.


Fit Ridge classifier with cv.
Training vectors, where n_samples is the number of samples and n_features is the number of features. When using GCV, will be cast to float64 if necessary.
Target values. Will be cast to X’s dtype if necessary.
Individual weights for each sample. If given a float, every sample will have the same weight.


Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.


Ridge regression with built-in cross-validation.
See glossary entry for cross-validation estimator.
By default, it performs efficient Leave-One-Out Cross-Validation.
Read more in the User Guide.
Array of alpha values to try. Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to 1 / (2C) in other linear models such as LogisticRegression or LinearSVC. If using Leave-One-Out cross-validation, alphas must be positive.
Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).
This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False.
A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). If None, the negative mean squared error if cv is ‘auto’ or None (i.e. when using leave-one-out cross-validation), and r2 score otherwise.
Determines the cross-validation splitting strategy. Possible inputs for cv are:
For integer/None inputs, if y is binary or multiclass, StratifiedKFold is used, else, KFold is used.
Refer User Guide for the various cross-validation strategies that can be used here.
Flag indicating which strategy to use when performing Leave-One-Out Cross-Validation. Options are:
The ‘auto’ mode is the default and is intended to pick the cheaper option of the two depending on the shape of the training data.
Flag indicating if the cross-validation values corresponding to each alpha should be stored in the cv_values_ attribute (see below). This flag is only compatible with cv=None (i.e. using Leave-One-Out Cross-Validation).
Flag indicating whether to optimize the alpha value (picked from the alphas parameter list) for each target separately (for multi-output settings: multiple prediction targets). When set to True, after fitting, the alpha_ attribute will contain a value for each target. When set to False, a single alpha is used for all targets.
New in version 0.24.
Cross-validation values for each alpha (only available if store_cv_values=True and cv=None). After fit() has been called, this attribute will contain the mean squared errors (by default) or the values of the {loss,score}_func function (if provided in the constructor).
Weight vector(s).
Independent term in decision function. Set to 0.0 if fit_intercept = False.
Estimated regularization parameter, or, if alpha_per_target=True, the estimated regularization parameter for each target.
Score of base estimator with best alpha, or, if alpha_per_target=True, a score for each target.
New in version 0.23.
See also
Ridge regression.
Ridge classifier.
Ridge classifier with built-in cross validation.
fit(X, y[, sample_weight])
Fit Ridge regression model with cv.
get_params([deep])
Get parameters for this estimator.
predict(X)
Predict using the linear model.
score(X, y[, sample_weight])
Return the coefficient of determination \(R^2\) of the prediction.
set_params(**params)
Set the parameters of this estimator.
Fit Ridge regression model with cv.
Training data. If using GCV, will be cast to float64 if necessary.
Target values. Will be cast to X’s dtype if necessary.
Individual weights for each sample. If given a float, every sample will have the same weight.
When sample_weight is provided, the selected hyperparameter may depend on whether we use leave-one-out cross-validation (cv=None or cv=’auto’) or another form of cross-validation, because only leave-one-out cross-validation takes the sample weights into account when computing the validation score.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Predict using the linear model.
Samples.
Returns predicted values.
Return the coefficient of determination \(R^2\) of the prediction.
The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
True values for X.
Sample weights.
\(R^2\) of self.predict(X) wrt. y.
The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Estimator parameters.
Estimator instance.


Ridge regression with built-in cross-validation.
See glossary entry for cross-validation estimator.
By default, it performs efficient Leave-One-Out Cross-Validation.
Read more in the User Guide.
Array of alpha values to try. Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to 1 / (2C) in other linear models such as LogisticRegression or LinearSVC. If using Leave-One-Out cross-validation, alphas must be positive.
Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).
This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False.
A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). If None, the negative mean squared error if cv is ‘auto’ or None (i.e. when using leave-one-out cross-validation), and r2 score otherwise.
Determines the cross-validation splitting strategy. Possible inputs for cv are:
For integer/None inputs, if y is binary or multiclass, StratifiedKFold is used, else, KFold is used.
Refer User Guide for the various cross-validation strategies that can be used here.
Flag indicating which strategy to use when performing Leave-One-Out Cross-Validation. Options are:
The ‘auto’ mode is the default and is intended to pick the cheaper option of the two depending on the shape of the training data.
Flag indicating if the cross-validation values corresponding to each alpha should be stored in the cv_values_ attribute (see below). This flag is only compatible with cv=None (i.e. using Leave-One-Out Cross-Validation).
Flag indicating whether to optimize the alpha value (picked from the alphas parameter list) for each target separately (for multi-output settings: multiple prediction targets). When set to True, after fitting, the alpha_ attribute will contain a value for each target. When set to False, a single alpha is used for all targets.
New in version 0.24.
Cross-validation values for each alpha (only available if store_cv_values=True and cv=None). After fit() has been called, this attribute will contain the mean squared errors (by default) or the values of the {loss,score}_func function (if provided in the constructor).
Weight vector(s).
Independent term in decision function. Set to 0.0 if fit_intercept = False.
Estimated regularization parameter, or, if alpha_per_target=True, the estimated regularization parameter for each target.
Score of base estimator with best alpha, or, if alpha_per_target=True, a score for each target.
New in version 0.23.
See also
Ridge regression.
Ridge classifier.
Ridge classifier with built-in cross validation.
fit(X, y[, sample_weight])
Fit Ridge regression model with cv.
get_params([deep])
Get parameters for this estimator.
predict(X)
Predict using the linear model.
score(X, y[, sample_weight])
Return the coefficient of determination \(R^2\) of the prediction.
set_params(**params)
Set the parameters of this estimator.
Fit Ridge regression model with cv.
Training data. If using GCV, will be cast to float64 if necessary.
Target values. Will be cast to X’s dtype if necessary.
Individual weights for each sample. If given a float, every sample will have the same weight.
When sample_weight is provided, the selected hyperparameter may depend on whether we use leave-one-out cross-validation (cv=None or cv=’auto’) or another form of cross-validation, because only leave-one-out cross-validation takes the sample weights into account when computing the validation score.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Predict using the linear model.
Samples.
Returns predicted values.
Return the coefficient of determination \(R^2\) of the prediction.
The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
True values for X.
Sample weights.
\(R^2\) of self.predict(X) wrt. y.
The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Estimator parameters.
Estimator instance.
Combine predictors using stacking
Common pitfalls in interpretation of coefficients of linear models
Face completion with a multi-output estimators
Effect of transforming the targets in regression model


Fit Ridge regression model with cv.
Training data. If using GCV, will be cast to float64 if necessary.
Target values. Will be cast to X’s dtype if necessary.
Individual weights for each sample. If given a float, every sample will have the same weight.
When sample_weight is provided, the selected hyperparameter may depend on whether we use leave-one-out cross-validation (cv=None or cv=’auto’) or another form of cross-validation, because only leave-one-out cross-validation takes the sample weights into account when computing the validation score.


Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Estimator parameters.
Estimator instance.


Solve the ridge equation by the method of normal equations.
Read more in the User Guide.
Training data
Target values
Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to 1 / (2C) in other linear models such as LogisticRegression or LinearSVC. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number.
Individual weights for each sample. If given a float, every sample will have the same weight. If sample_weight is not None and solver=’auto’, the solver will be set to ‘cholesky’.
New in version 0.17.
Solver to use in the computational routines:
All last five solvers support both dense and sparse data. However, only ‘sag’ and ‘sparse_cg’ supports sparse input when fit_intercept is True.
New in version 0.17: Stochastic Average Gradient descent solver.
New in version 0.19: SAGA solver.
Maximum number of iterations for conjugate gradient solver. For the ‘sparse_cg’ and ‘lsqr’ solvers, the default value is determined by scipy.sparse.linalg. For ‘sag’ and saga solver, the default value is 1000.
Precision of the solution.
Verbosity level. Setting verbose > 0 will display additional information depending on the solver used.
Used when solver == ‘sag’ or ‘saga’ to shuffle the data. See Glossary for details.
If True, the method also returns n_iter, the actual number of iteration performed by the solver.
New in version 0.17.
If True and if X is sparse, the method also returns the intercept, and the solver is automatically changed to ‘sag’. This is only a temporary fix for fitting the intercept with sparse data. For dense data, use sklearn.linear_model._preprocess_data before your regression.
New in version 0.17.
If False, the input arrays X and y will not be checked.
New in version 0.21.
Weight vector(s).
The actual number of iteration performed by the solver. Only returned if return_n_iter is True.
The intercept of the model. Only returned if return_intercept is True and if X is a scipy sparse array.
This function won’t compute the intercept.


Linear classifiers (SVM, logistic regression, etc.) with SGD training.
This estimator implements regularized linear models with stochastic gradient descent (SGD) learning: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate). SGD allows minibatch (online/out-of-core) learning via the partial_fit method. For best results using the default learning rate schedule, the data should have zero mean and unit variance.
This implementation works with data represented as dense or sparse arrays of floating point values for the features. The model it fits can be controlled with the loss parameter; by default, it fits a linear support vector machine (SVM).
The regularizer is a penalty added to the loss function that shrinks model parameters towards the zero vector using either the squared euclidean norm L2 or the absolute norm L1 or a combination of both (Elastic Net). If the parameter update crosses the 0.0 value because of the regularizer, the update is truncated to 0.0 to allow for learning sparse models and achieve online feature selection.
Read more in the User Guide.
The loss function to be used. Defaults to ‘hinge’, which gives a linear SVM.
The possible options are ‘hinge’, ‘log’, ‘modified_huber’, ‘squared_hinge’, ‘perceptron’, or a regression loss: ‘squared_loss’, ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’.
The ‘log’ loss gives logistic regression, a probabilistic classifier. ‘modified_huber’ is another smooth loss that brings tolerance to outliers as well as probability estimates. ‘squared_hinge’ is like hinge but is quadratically penalized. ‘perceptron’ is the linear loss used by the perceptron algorithm. The other losses are designed for regression but can be useful in classification as well; see SGDRegressor for a description.
More details about the losses formulas can be found in the User Guide.
The penalty (aka regularization term) to be used. Defaults to ‘l2’ which is the standard regularizer for linear SVM models. ‘l1’ and ‘elasticnet’ might bring sparsity to the model (feature selection) not achievable with ‘l2’.
Constant that multiplies the regularization term. The higher the value, the stronger the regularization. Also used to compute the learning rate when set to learning_rate is set to ‘optimal’.
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1. Only used if penalty is ‘elasticnet’.
Whether the intercept should be estimated or not. If False, the data is assumed to be already centered.
The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the fit method, and not the partial_fit method.
New in version 0.19.
The stopping criterion. If it is not None, training will stop when (loss > best_loss - tol) for n_iter_no_change consecutive epochs.
New in version 0.19.
Whether or not the training data should be shuffled after each epoch.
The verbosity level.
Epsilon in the epsilon-insensitive loss functions; only if loss is ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’. For ‘huber’, determines the threshold at which it becomes less important to get the prediction exactly right. For epsilon-insensitive, any differences between the current prediction and the correct label are ignored if they are less than this threshold.
The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
Used for shuffling the data, when shuffle is set to True. Pass an int for reproducible output across multiple function calls. See Glossary.
The learning rate schedule:
‘adaptive’: eta = eta0, as long as the training keeps decreasing. Each time n_iter_no_change consecutive epochs fail to decrease the training loss by tol or fail to increase validation score by tol if early_stopping is True, the current learning rate is divided by 5.
New in version 0.20: Added ‘adaptive’ option
The initial learning rate for the ‘constant’, ‘invscaling’ or ‘adaptive’ schedules. The default value is 0.0 as eta0 is not used by the default schedule ‘optimal’.
The exponent for inverse scaling learning rate [default 0.5].
Whether to use early stopping to terminate training when validation score is not improving. If set to True, it will automatically set aside a stratified fraction of training data as validation and terminate training when validation score returned by the score method is not improving by at least tol for n_iter_no_change consecutive epochs.
New in version 0.20: Added ‘early_stopping’ option
The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if early_stopping is True.
New in version 0.20: Added ‘validation_fraction’ option
Number of iterations with no improvement to wait before early stopping.
New in version 0.20: Added ‘n_iter_no_change’ option
Preset for the class_weight fit parameter.
Weights associated with classes. If not given, all classes are supposed to have weight one.
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).
When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary.
Repeatedly calling fit or partial_fit when warm_start is True can result in a different solution than when calling fit a single time because of the way the data is shuffled. If a dynamic learning rate is used, the learning rate is adapted depending on the number of samples already seen. Calling fit resets this counter, while partial_fit will result in increasing the existing counter.
When set to True, computes the averaged SGD weights accross all updates and stores the result in the coef_ attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples.
Weights assigned to the features.
Constants in decision function.
The actual number of iterations before reaching the stopping criterion. For multiclass fits, it is the maximum over every binary fit.
Number of weight updates performed during training. Same as (n_iter_ * n_samples).
See also
Linear support vector classification.
Logistic regression.
Inherits from SGDClassifier. Perceptron() is equivalent to SGDClassifier(loss="perceptron", eta0=1, learning_rate="constant", penalty=None).
decision_function(X)
Predict confidence scores for samples.
densify()
Convert coefficient matrix to dense array format.
fit(X, y[, coef_init, intercept_init, …])
Fit linear model with Stochastic Gradient Descent.
get_params([deep])
Get parameters for this estimator.
partial_fit(X, y[, classes, sample_weight])
Perform one epoch of stochastic gradient descent on given samples.
predict(X)
Predict class labels for samples in X.
score(X, y[, sample_weight])
Return the mean accuracy on the given test data and labels.
set_params(**kwargs)
Set and validate the parameters of estimator.
sparsify()
Convert coefficient matrix to sparse format.
Predict confidence scores for samples.
The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.
Samples.
Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.
Convert coefficient matrix to dense array format.
Converts the coef_ member (back) to a numpy.ndarray. This is the default format of coef_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.
Fitted estimator.
Fit linear model with Stochastic Gradient Descent.
Training data.
Target values.
The initial coefficients to warm-start the optimization.
The initial intercept to warm-start the optimization.
Weights applied to individual samples. If not provided, uniform weights are assumed. These weights will be multiplied with class_weight (passed through the constructor) if class_weight is specified.
Returns an instance of self.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses max_iter = 1. Therefore, it is not guaranteed that a minimum of the cost function is reached after calling it once. Matters such as objective convergence and early stopping should be handled by the user.
Subset of the training data.
Subset of the target values.
Classes across all calls to partial_fit. Can be obtained by via np.unique(y_all), where y_all is the target vector of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.
Weights applied to individual samples. If not provided, uniform weights are assumed.
Returns an instance of self.
Predict class labels for samples in X.
Samples.
Predicted class label per sample.
Log of probability estimates.
This method is only available for log loss and modified Huber loss.
When loss=”modified_huber”, probability estimates may be hard zeros and ones, so taking the logarithm is not possible.
See predict_proba for details.
Input data for prediction.
Returns the log-probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.
Probability estimates.
This method is only available for log loss and modified Huber loss.
Multiclass probability estimates are derived from binary (one-vs.-rest) estimates by simple normalization, as recommended by Zadrozny and Elkan.
Binary probability estimates for loss=”modified_huber” are given by (clip(decision_function(X), -1, 1) + 1) / 2. For other loss functions it is necessary to perform proper probability calibration by wrapping the classifier with CalibratedClassifierCV instead.
Input data for prediction.
Returns the probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.
Zadrozny and Elkan, “Transforming classifier scores into multiclass probability estimates”, SIGKDD’02, http://www.research.ibm.com/people/z/zadrozny/kdd2002-Transf.pdf
The justification for the formula in the loss=”modified_huber” case is in the appendix B in: http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Test samples.
True labels for X.
Sample weights.
Mean accuracy of self.predict(X) wrt. y.
Set and validate the parameters of estimator.
Estimator parameters.
Estimator instance.
Convert coefficient matrix to sparse format.
Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.
The intercept_ member is not converted.
Fitted estimator.
For non-sparse models, i.e. when there are not many zeros in coef_, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with (coef_ == 0).sum(), must be more than 50% for this to provide significant benefits.
After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.


Linear classifiers (SVM, logistic regression, etc.) with SGD training.
This estimator implements regularized linear models with stochastic gradient descent (SGD) learning: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate). SGD allows minibatch (online/out-of-core) learning via the partial_fit method. For best results using the default learning rate schedule, the data should have zero mean and unit variance.
This implementation works with data represented as dense or sparse arrays of floating point values for the features. The model it fits can be controlled with the loss parameter; by default, it fits a linear support vector machine (SVM).
The regularizer is a penalty added to the loss function that shrinks model parameters towards the zero vector using either the squared euclidean norm L2 or the absolute norm L1 or a combination of both (Elastic Net). If the parameter update crosses the 0.0 value because of the regularizer, the update is truncated to 0.0 to allow for learning sparse models and achieve online feature selection.
Read more in the User Guide.
The loss function to be used. Defaults to ‘hinge’, which gives a linear SVM.
The possible options are ‘hinge’, ‘log’, ‘modified_huber’, ‘squared_hinge’, ‘perceptron’, or a regression loss: ‘squared_loss’, ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’.
The ‘log’ loss gives logistic regression, a probabilistic classifier. ‘modified_huber’ is another smooth loss that brings tolerance to outliers as well as probability estimates. ‘squared_hinge’ is like hinge but is quadratically penalized. ‘perceptron’ is the linear loss used by the perceptron algorithm. The other losses are designed for regression but can be useful in classification as well; see SGDRegressor for a description.
More details about the losses formulas can be found in the User Guide.
The penalty (aka regularization term) to be used. Defaults to ‘l2’ which is the standard regularizer for linear SVM models. ‘l1’ and ‘elasticnet’ might bring sparsity to the model (feature selection) not achievable with ‘l2’.
Constant that multiplies the regularization term. The higher the value, the stronger the regularization. Also used to compute the learning rate when set to learning_rate is set to ‘optimal’.
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1. Only used if penalty is ‘elasticnet’.
Whether the intercept should be estimated or not. If False, the data is assumed to be already centered.
The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the fit method, and not the partial_fit method.
New in version 0.19.
The stopping criterion. If it is not None, training will stop when (loss > best_loss - tol) for n_iter_no_change consecutive epochs.
New in version 0.19.
Whether or not the training data should be shuffled after each epoch.
The verbosity level.
Epsilon in the epsilon-insensitive loss functions; only if loss is ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’. For ‘huber’, determines the threshold at which it becomes less important to get the prediction exactly right. For epsilon-insensitive, any differences between the current prediction and the correct label are ignored if they are less than this threshold.
The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
Used for shuffling the data, when shuffle is set to True. Pass an int for reproducible output across multiple function calls. See Glossary.
The learning rate schedule:
‘adaptive’: eta = eta0, as long as the training keeps decreasing. Each time n_iter_no_change consecutive epochs fail to decrease the training loss by tol or fail to increase validation score by tol if early_stopping is True, the current learning rate is divided by 5.
New in version 0.20: Added ‘adaptive’ option
The initial learning rate for the ‘constant’, ‘invscaling’ or ‘adaptive’ schedules. The default value is 0.0 as eta0 is not used by the default schedule ‘optimal’.
The exponent for inverse scaling learning rate [default 0.5].
Whether to use early stopping to terminate training when validation score is not improving. If set to True, it will automatically set aside a stratified fraction of training data as validation and terminate training when validation score returned by the score method is not improving by at least tol for n_iter_no_change consecutive epochs.
New in version 0.20: Added ‘early_stopping’ option
The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if early_stopping is True.
New in version 0.20: Added ‘validation_fraction’ option
Number of iterations with no improvement to wait before early stopping.
New in version 0.20: Added ‘n_iter_no_change’ option
Preset for the class_weight fit parameter.
Weights associated with classes. If not given, all classes are supposed to have weight one.
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).
When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary.
Repeatedly calling fit or partial_fit when warm_start is True can result in a different solution than when calling fit a single time because of the way the data is shuffled. If a dynamic learning rate is used, the learning rate is adapted depending on the number of samples already seen. Calling fit resets this counter, while partial_fit will result in increasing the existing counter.
When set to True, computes the averaged SGD weights accross all updates and stores the result in the coef_ attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples.
Weights assigned to the features.
Constants in decision function.
The actual number of iterations before reaching the stopping criterion. For multiclass fits, it is the maximum over every binary fit.
Number of weight updates performed during training. Same as (n_iter_ * n_samples).
See also
Linear support vector classification.
Logistic regression.
Inherits from SGDClassifier. Perceptron() is equivalent to SGDClassifier(loss="perceptron", eta0=1, learning_rate="constant", penalty=None).
decision_function(X)
Predict confidence scores for samples.
densify()
Convert coefficient matrix to dense array format.
fit(X, y[, coef_init, intercept_init, …])
Fit linear model with Stochastic Gradient Descent.
get_params([deep])
Get parameters for this estimator.
partial_fit(X, y[, classes, sample_weight])
Perform one epoch of stochastic gradient descent on given samples.
predict(X)
Predict class labels for samples in X.
score(X, y[, sample_weight])
Return the mean accuracy on the given test data and labels.
set_params(**kwargs)
Set and validate the parameters of estimator.
sparsify()
Convert coefficient matrix to sparse format.
Predict confidence scores for samples.
The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.
Samples.
Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.
Convert coefficient matrix to dense array format.
Converts the coef_ member (back) to a numpy.ndarray. This is the default format of coef_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.
Fitted estimator.
Fit linear model with Stochastic Gradient Descent.
Training data.
Target values.
The initial coefficients to warm-start the optimization.
The initial intercept to warm-start the optimization.
Weights applied to individual samples. If not provided, uniform weights are assumed. These weights will be multiplied with class_weight (passed through the constructor) if class_weight is specified.
Returns an instance of self.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses max_iter = 1. Therefore, it is not guaranteed that a minimum of the cost function is reached after calling it once. Matters such as objective convergence and early stopping should be handled by the user.
Subset of the training data.
Subset of the target values.
Classes across all calls to partial_fit. Can be obtained by via np.unique(y_all), where y_all is the target vector of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.
Weights applied to individual samples. If not provided, uniform weights are assumed.
Returns an instance of self.
Predict class labels for samples in X.
Samples.
Predicted class label per sample.
Log of probability estimates.
This method is only available for log loss and modified Huber loss.
When loss=”modified_huber”, probability estimates may be hard zeros and ones, so taking the logarithm is not possible.
See predict_proba for details.
Input data for prediction.
Returns the log-probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.
Probability estimates.
This method is only available for log loss and modified Huber loss.
Multiclass probability estimates are derived from binary (one-vs.-rest) estimates by simple normalization, as recommended by Zadrozny and Elkan.
Binary probability estimates for loss=”modified_huber” are given by (clip(decision_function(X), -1, 1) + 1) / 2. For other loss functions it is necessary to perform proper probability calibration by wrapping the classifier with CalibratedClassifierCV instead.
Input data for prediction.
Returns the probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.
Zadrozny and Elkan, “Transforming classifier scores into multiclass probability estimates”, SIGKDD’02, http://www.research.ibm.com/people/z/zadrozny/kdd2002-Transf.pdf
The justification for the formula in the loss=”modified_huber” case is in the appendix B in: http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Test samples.
True labels for X.
Sample weights.
Mean accuracy of self.predict(X) wrt. y.
Set and validate the parameters of estimator.
Estimator parameters.
Estimator instance.
Convert coefficient matrix to sparse format.
Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.
The intercept_ member is not converted.
Fitted estimator.
For non-sparse models, i.e. when there are not many zeros in coef_, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with (coef_ == 0).sum(), must be more than 50% for this to provide significant benefits.
After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.
Model Complexity Influence
Out-of-core classification of text documents
SGD: Maximum margin separating hyperplane
SGD: convex loss functions
SGD: Penalties
SGD: Weighted samples
Comparing various online solvers
Plot multi-class SGD on the iris dataset
Early stopping of Stochastic Gradient Descent
Explicit feature map approximation for RBF kernels
Comparing randomized search and grid search for hyperparameter estimation
Sample pipeline for text feature extraction and evaluation
Semi-supervised Classification on a Text Dataset
Classification of text documents using sparse features


Convert coefficient matrix to dense array format.
Converts the coef_ member (back) to a numpy.ndarray. This is the default format of coef_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.
Fitted estimator.


Fit linear model with Stochastic Gradient Descent.
Training data.
Target values.
The initial coefficients to warm-start the optimization.
The initial intercept to warm-start the optimization.
Weights applied to individual samples. If not provided, uniform weights are assumed. These weights will be multiplied with class_weight (passed through the constructor) if class_weight is specified.
Returns an instance of self.


Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses max_iter = 1. Therefore, it is not guaranteed that a minimum of the cost function is reached after calling it once. Matters such as objective convergence and early stopping should be handled by the user.
Subset of the training data.
Subset of the target values.
Classes across all calls to partial_fit. Can be obtained by via np.unique(y_all), where y_all is the target vector of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.
Weights applied to individual samples. If not provided, uniform weights are assumed.
Returns an instance of self.


Log of probability estimates.
This method is only available for log loss and modified Huber loss.
When loss=”modified_huber”, probability estimates may be hard zeros and ones, so taking the logarithm is not possible.
See predict_proba for details.
Input data for prediction.
Returns the log-probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.


Probability estimates.
This method is only available for log loss and modified Huber loss.
Multiclass probability estimates are derived from binary (one-vs.-rest) estimates by simple normalization, as recommended by Zadrozny and Elkan.
Binary probability estimates for loss=”modified_huber” are given by (clip(decision_function(X), -1, 1) + 1) / 2. For other loss functions it is necessary to perform proper probability calibration by wrapping the classifier with CalibratedClassifierCV instead.
Input data for prediction.
Returns the probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.
Zadrozny and Elkan, “Transforming classifier scores into multiclass probability estimates”, SIGKDD’02, http://www.research.ibm.com/people/z/zadrozny/kdd2002-Transf.pdf
The justification for the formula in the loss=”modified_huber” case is in the appendix B in: http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf


Set and validate the parameters of estimator.
Estimator parameters.
Estimator instance.


Convert coefficient matrix to sparse format.
Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.
The intercept_ member is not converted.
Fitted estimator.
For non-sparse models, i.e. when there are not many zeros in coef_, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with (coef_ == 0).sum(), must be more than 50% for this to provide significant benefits.
After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.


Linear model fitted by minimizing a regularized empirical loss with SGD
SGD stands for Stochastic Gradient Descent: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate).
The regularizer is a penalty added to the loss function that shrinks model parameters towards the zero vector using either the squared euclidean norm L2 or the absolute norm L1 or a combination of both (Elastic Net). If the parameter update crosses the 0.0 value because of the regularizer, the update is truncated to 0.0 to allow for learning sparse models and achieve online feature selection.
This implementation works with data represented as dense numpy arrays of floating point values for the features.
Read more in the User Guide.
The loss function to be used. The possible values are ‘squared_loss’, ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’
The ‘squared_loss’ refers to the ordinary least squares fit. ‘huber’ modifies ‘squared_loss’ to focus less on getting outliers correct by switching from squared to linear loss past a distance of epsilon. ‘epsilon_insensitive’ ignores errors less than epsilon and is linear past that; this is the loss function used in SVR. ‘squared_epsilon_insensitive’ is the same but becomes squared loss past a tolerance of epsilon.
More details about the losses formulas can be found in the User Guide.
The penalty (aka regularization term) to be used. Defaults to ‘l2’ which is the standard regularizer for linear SVM models. ‘l1’ and ‘elasticnet’ might bring sparsity to the model (feature selection) not achievable with ‘l2’.
Constant that multiplies the regularization term. The higher the value, the stronger the regularization. Also used to compute the learning rate when set to learning_rate is set to ‘optimal’.
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1. Only used if penalty is ‘elasticnet’.
Whether the intercept should be estimated or not. If False, the data is assumed to be already centered.
The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the fit method, and not the partial_fit method.
New in version 0.19.
The stopping criterion. If it is not None, training will stop when (loss > best_loss - tol) for n_iter_no_change consecutive epochs.
New in version 0.19.
Whether or not the training data should be shuffled after each epoch.
The verbosity level.
Epsilon in the epsilon-insensitive loss functions; only if loss is ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’. For ‘huber’, determines the threshold at which it becomes less important to get the prediction exactly right. For epsilon-insensitive, any differences between the current prediction and the correct label are ignored if they are less than this threshold.
Used for shuffling the data, when shuffle is set to True. Pass an int for reproducible output across multiple function calls. See Glossary.
The learning rate schedule:
‘adaptive’: eta = eta0, as long as the training keeps decreasing. Each time n_iter_no_change consecutive epochs fail to decrease the training loss by tol or fail to increase validation score by tol if early_stopping is True, the current learning rate is divided by 5.
New in version 0.20: Added ‘adaptive’ option
The initial learning rate for the ‘constant’, ‘invscaling’ or ‘adaptive’ schedules. The default value is 0.01.
The exponent for inverse scaling learning rate.
Whether to use early stopping to terminate training when validation score is not improving. If set to True, it will automatically set aside a fraction of training data as validation and terminate training when validation score returned by the score method is not improving by at least tol for n_iter_no_change consecutive epochs.
New in version 0.20: Added ‘early_stopping’ option
The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if early_stopping is True.
New in version 0.20: Added ‘validation_fraction’ option
Number of iterations with no improvement to wait before early stopping.
New in version 0.20: Added ‘n_iter_no_change’ option
When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary.
Repeatedly calling fit or partial_fit when warm_start is True can result in a different solution than when calling fit a single time because of the way the data is shuffled. If a dynamic learning rate is used, the learning rate is adapted depending on the number of samples already seen. Calling fit resets this counter, while partial_fit will result in increasing the existing counter.
When set to True, computes the averaged SGD weights accross all updates and stores the result in the coef_ attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples.
Weights assigned to the features.
The intercept term.
Averaged weights assigned to the features. Only available if average=True.
Deprecated since version 0.23: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).
The averaged intercept term. Only available if average=True.
Deprecated since version 0.23: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).
The actual number of iterations before reaching the stopping criterion.
Number of weight updates performed during training. Same as (n_iter_ * n_samples).
See also
densify()
Convert coefficient matrix to dense array format.
fit(X, y[, coef_init, intercept_init, …])
Fit linear model with Stochastic Gradient Descent.
get_params([deep])
Get parameters for this estimator.
partial_fit(X, y[, sample_weight])
Perform one epoch of stochastic gradient descent on given samples.
predict(X)
Predict using the linear model
score(X, y[, sample_weight])
Return the coefficient of determination \(R^2\) of the prediction.
set_params(**kwargs)
Set and validate the parameters of estimator.
sparsify()
Convert coefficient matrix to sparse format.
Convert coefficient matrix to dense array format.
Converts the coef_ member (back) to a numpy.ndarray. This is the default format of coef_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.
Fitted estimator.
Fit linear model with Stochastic Gradient Descent.
Training data
Target values
The initial coefficients to warm-start the optimization.
The initial intercept to warm-start the optimization.
Weights applied to individual samples (1. for unweighted).
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses max_iter = 1. Therefore, it is not guaranteed that a minimum of the cost function is reached after calling it once. Matters such as objective convergence and early stopping should be handled by the user.
Subset of training data
Subset of target values
Weights applied to individual samples. If not provided, uniform weights are assumed.
Predict using the linear model
Predicted target values per element in X.
Return the coefficient of determination \(R^2\) of the prediction.
The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
True values for X.
Sample weights.
\(R^2\) of self.predict(X) wrt. y.
The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).
Set and validate the parameters of estimator.
Estimator parameters.
Estimator instance.
Convert coefficient matrix to sparse format.
Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.
The intercept_ member is not converted.
Fitted estimator.
For non-sparse models, i.e. when there are not many zeros in coef_, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with (coef_ == 0).sum(), must be more than 50% for this to provide significant benefits.
After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.


Linear model fitted by minimizing a regularized empirical loss with SGD
SGD stands for Stochastic Gradient Descent: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate).
The regularizer is a penalty added to the loss function that shrinks model parameters towards the zero vector using either the squared euclidean norm L2 or the absolute norm L1 or a combination of both (Elastic Net). If the parameter update crosses the 0.0 value because of the regularizer, the update is truncated to 0.0 to allow for learning sparse models and achieve online feature selection.
This implementation works with data represented as dense numpy arrays of floating point values for the features.
Read more in the User Guide.
The loss function to be used. The possible values are ‘squared_loss’, ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’
The ‘squared_loss’ refers to the ordinary least squares fit. ‘huber’ modifies ‘squared_loss’ to focus less on getting outliers correct by switching from squared to linear loss past a distance of epsilon. ‘epsilon_insensitive’ ignores errors less than epsilon and is linear past that; this is the loss function used in SVR. ‘squared_epsilon_insensitive’ is the same but becomes squared loss past a tolerance of epsilon.
More details about the losses formulas can be found in the User Guide.
The penalty (aka regularization term) to be used. Defaults to ‘l2’ which is the standard regularizer for linear SVM models. ‘l1’ and ‘elasticnet’ might bring sparsity to the model (feature selection) not achievable with ‘l2’.
Constant that multiplies the regularization term. The higher the value, the stronger the regularization. Also used to compute the learning rate when set to learning_rate is set to ‘optimal’.
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1. Only used if penalty is ‘elasticnet’.
Whether the intercept should be estimated or not. If False, the data is assumed to be already centered.
The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the fit method, and not the partial_fit method.
New in version 0.19.
The stopping criterion. If it is not None, training will stop when (loss > best_loss - tol) for n_iter_no_change consecutive epochs.
New in version 0.19.
Whether or not the training data should be shuffled after each epoch.
The verbosity level.
Epsilon in the epsilon-insensitive loss functions; only if loss is ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’. For ‘huber’, determines the threshold at which it becomes less important to get the prediction exactly right. For epsilon-insensitive, any differences between the current prediction and the correct label are ignored if they are less than this threshold.
Used for shuffling the data, when shuffle is set to True. Pass an int for reproducible output across multiple function calls. See Glossary.
The learning rate schedule:
‘adaptive’: eta = eta0, as long as the training keeps decreasing. Each time n_iter_no_change consecutive epochs fail to decrease the training loss by tol or fail to increase validation score by tol if early_stopping is True, the current learning rate is divided by 5.
New in version 0.20: Added ‘adaptive’ option
The initial learning rate for the ‘constant’, ‘invscaling’ or ‘adaptive’ schedules. The default value is 0.01.
The exponent for inverse scaling learning rate.
Whether to use early stopping to terminate training when validation score is not improving. If set to True, it will automatically set aside a fraction of training data as validation and terminate training when validation score returned by the score method is not improving by at least tol for n_iter_no_change consecutive epochs.
New in version 0.20: Added ‘early_stopping’ option
The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if early_stopping is True.
New in version 0.20: Added ‘validation_fraction’ option
Number of iterations with no improvement to wait before early stopping.
New in version 0.20: Added ‘n_iter_no_change’ option
When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary.
Repeatedly calling fit or partial_fit when warm_start is True can result in a different solution than when calling fit a single time because of the way the data is shuffled. If a dynamic learning rate is used, the learning rate is adapted depending on the number of samples already seen. Calling fit resets this counter, while partial_fit will result in increasing the existing counter.
When set to True, computes the averaged SGD weights accross all updates and stores the result in the coef_ attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples.
Weights assigned to the features.
The intercept term.
Averaged weights assigned to the features. Only available if average=True.
Deprecated since version 0.23: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).
The averaged intercept term. Only available if average=True.
Deprecated since version 0.23: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).
The actual number of iterations before reaching the stopping criterion.
Number of weight updates performed during training. Same as (n_iter_ * n_samples).
See also
densify()
Convert coefficient matrix to dense array format.
fit(X, y[, coef_init, intercept_init, …])
Fit linear model with Stochastic Gradient Descent.
get_params([deep])
Get parameters for this estimator.
partial_fit(X, y[, sample_weight])
Perform one epoch of stochastic gradient descent on given samples.
predict(X)
Predict using the linear model
score(X, y[, sample_weight])
Return the coefficient of determination \(R^2\) of the prediction.
set_params(**kwargs)
Set and validate the parameters of estimator.
sparsify()
Convert coefficient matrix to sparse format.
Convert coefficient matrix to dense array format.
Converts the coef_ member (back) to a numpy.ndarray. This is the default format of coef_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.
Fitted estimator.
Fit linear model with Stochastic Gradient Descent.
Training data
Target values
The initial coefficients to warm-start the optimization.
The initial intercept to warm-start the optimization.
Weights applied to individual samples (1. for unweighted).
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses max_iter = 1. Therefore, it is not guaranteed that a minimum of the cost function is reached after calling it once. Matters such as objective convergence and early stopping should be handled by the user.
Subset of training data
Subset of target values
Weights applied to individual samples. If not provided, uniform weights are assumed.
Predict using the linear model
Predicted target values per element in X.
Return the coefficient of determination \(R^2\) of the prediction.
The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
True values for X.
Sample weights.
\(R^2\) of self.predict(X) wrt. y.
The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).
Set and validate the parameters of estimator.
Estimator parameters.
Estimator instance.
Convert coefficient matrix to sparse format.
Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.
The intercept_ member is not converted.
Fitted estimator.
For non-sparse models, i.e. when there are not many zeros in coef_, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with (coef_ == 0).sum(), must be more than 50% for this to provide significant benefits.
After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.
Prediction Latency
SGD: Penalties


Fit linear model with Stochastic Gradient Descent.
Training data
Target values
The initial coefficients to warm-start the optimization.
The initial intercept to warm-start the optimization.
Weights applied to individual samples (1. for unweighted).


Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.


Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses max_iter = 1. Therefore, it is not guaranteed that a minimum of the cost function is reached after calling it once. Matters such as objective convergence and early stopping should be handled by the user.
Subset of training data
Subset of target values
Weights applied to individual samples. If not provided, uniform weights are assumed.


Predict using the linear model
Predicted target values per element in X.


Return the coefficient of determination \(R^2\) of the prediction.
The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
True values for X.
Sample weights.
\(R^2\) of self.predict(X) wrt. y.
The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).


Theil-Sen Estimator: robust multivariate regression model.
The algorithm calculates least square solutions on subsets with size n_subsamples of the samples in X. Any value of n_subsamples between the number of features and samples leads to an estimator with a compromise between robustness and efficiency. Since the number of least square solutions is “n_samples choose n_subsamples”, it can be extremely large and can therefore be limited with max_subpopulation. If this limit is reached, the subsets are chosen randomly. In a final step, the spatial median (or L1 median) is calculated of all least square solutions.
Read more in the User Guide.
Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations.
If True, X will be copied; else, it may be overwritten.
Instead of computing with a set of cardinality ‘n choose k’, where n is the number of samples and k is the number of subsamples (at least number of features), consider only a stochastic subpopulation of a given maximal size if ‘n choose k’ is larger than max_subpopulation. For other than small problem sizes this parameter will determine memory usage and runtime if n_subsamples is not changed.
Number of samples to calculate the parameters. This is at least the number of features (plus 1 if fit_intercept=True) and the number of samples as a maximum. A lower number leads to a higher breakdown point and a low efficiency while a high number leads to a low breakdown point and a high efficiency. If None, take the minimum number of subsamples leading to maximal robustness. If n_subsamples is set to n_samples, Theil-Sen is identical to least squares.
Maximum number of iterations for the calculation of spatial median.
Tolerance when calculating spatial median.
A random number generator instance to define the state of the random permutations generator. Pass an int for reproducible output across multiple function calls. See Glossary
Number of CPUs to use during the cross validation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
Verbose mode when fitting the model.
Coefficients of the regression model (median of distribution).
Estimated intercept of regression model.
Approximated breakdown point.
Number of iterations needed for the spatial median.
Number of combinations taken into account from ‘n choose k’, where n is the number of samples and k is the number of subsamples.
fit(X, y)
Fit linear model.
get_params([deep])
Get parameters for this estimator.
predict(X)
Predict using the linear model.
score(X, y[, sample_weight])
Return the coefficient of determination \(R^2\) of the prediction.
set_params(**params)
Set the parameters of this estimator.
Fit linear model.
Training data.
Target values.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Predict using the linear model.
Samples.
Returns predicted values.
Return the coefficient of determination \(R^2\) of the prediction.
The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
True values for X.
Sample weights.
\(R^2\) of self.predict(X) wrt. y.
The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Estimator parameters.
Estimator instance.