LinearRegression (original) (raw)
class sklearn.linear_model.LinearRegression(*, fit_intercept=True, copy_X=True, n_jobs=None, positive=False)[source]#
Ordinary least squares Linear Regression.
LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.
Parameters:
fit_interceptbool, default=True
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).
copy_Xbool, default=True
If True, X will be copied; else, it may be overwritten.
n_jobsint, default=None
The number of jobs to use for the computation. This will only provide speedup in case of sufficiently large problems, that is if firstlyn_targets > 1 and secondly X is sparse or if positive is set to True. None means 1 unless in ajoblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
positivebool, default=False
When set to True, forces the coefficients to be positive. This option is only supported for dense arrays.
Added in version 0.24.
Attributes:
**coef_**array of shape (n_features, ) or (n_targets, n_features)
Estimated coefficients for the linear regression problem. If multiple targets are passed during the fit (y 2D), this is a 2D array of shape (n_targets, n_features), while if only one target is passed, this is a 1D array of length n_features.
**rank_**int
Rank of matrix X. Only available when X is dense.
**singular_**array of shape (min(X, y),)
Singular values of X. Only available when X is dense.
**intercept_**float or array of shape (n_targets,)
Independent term in the linear model. Set to 0.0 iffit_intercept = False.
**n_features_in_**int
Number of features seen during fit.
Added in version 0.24.
**feature_names_in_**ndarray of shape (n_features_in_,)
Names of features seen during fit. Defined only when Xhas feature names that are all strings.
Added in version 1.0.
See also
Ridge regression addresses some of the problems of Ordinary Least Squares by imposing a penalty on the size of the coefficients with l2 regularization.
The Lasso is a linear model that estimates sparse coefficients with l1 regularization.
Elastic-Net is a linear regression model trained with both l1 and l2 -norm regularization of the coefficients.
Notes
From the implementation point of view, this is just plain Ordinary Least Squares (scipy.linalg.lstsq) or Non Negative Least Squares (scipy.optimize.nnls) wrapped as a predictor object.
Examples
import numpy as np from sklearn.linear_model import LinearRegression X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = 1 * x_0 + 2 * x_1 + 3
y = np.dot(X, np.array([1, 2])) + 3 reg = LinearRegression().fit(X, y) reg.score(X, y) 1.0 reg.coef_ array([1., 2.]) reg.intercept_ np.float64(3.0...) reg.predict(np.array([[3, 5]])) array([16.])
fit(X, y, sample_weight=None)[source]#
Fit linear model.
Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
yarray-like of shape (n_samples,) or (n_samples, n_targets)
Target values. Will be cast to X’s dtype if necessary.
sample_weightarray-like of shape (n_samples,), default=None
Individual weights for each sample.
Added in version 0.17: parameter sample_weight support to LinearRegression.
Returns:
selfobject
Fitted Estimator.
get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Returns:
routingMetadataRequest
A MetadataRequest encapsulating routing information.
get_params(deep=True)[source]#
Get parameters for this estimator.
Parameters:
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:
paramsdict
Parameter names mapped to their values.
Predict using the linear model.
Parameters:
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples.
Returns:
Carray, shape (n_samples,)
Returns predicted values.
score(X, y, sample_weight=None)[source]#
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) 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.
Parameters:
Xarray-like of shape (n_samples, n_features)
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_fittedis the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
Returns:
scorefloat
\(R^2\) of self.predict(X) w.r.t. y.
Notes
The \(R^2\) score used when calling score on a regressor usesmultioutput='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 forMultiOutputRegressor).
set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → LinearRegression[source]#
Request metadata passed to the fit method.
Note that this method is only relevant ifenable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
True: metadata is requested, and passed tofitif provided. The request is ignored if metadata is not provided.False: metadata is not requested and the meta-estimator will not pass it tofit.None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.
Added in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside aPipeline. Otherwise it has no effect.
Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in fit.
Returns:
selfobject
The updated object.
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.
Parameters:
**paramsdict
Estimator parameters.
Returns:
selfestimator instance
Estimator instance.
set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → LinearRegression[source]#
Request metadata passed to the score method.
Note that this method is only relevant ifenable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
True: metadata is requested, and passed toscoreif provided. The request is ignored if metadata is not provided.False: metadata is not requested and the meta-estimator will not pass it toscore.None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.
Added in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside aPipeline. Otherwise it has no effect.
Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in score.
Returns:
selfobject
The updated object.