Linear regression coefficients python

sklearn.linear_model .LinearRegression¶

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_intercept bool, 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_X bool, default=True

If True, X will be copied; else, it may be overwritten.

n_jobs int, 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 firstly n_targets > 1 and secondly X is sparse or if positive is set to True . None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

positive bool, default=False

When set to True , forces the coefficients to be positive. This option is only supported for dense arrays.

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 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 if fit_intercept = False .

n_features_in_ int

Number of features seen during fit .

Names of features seen during fit . Defined only when X has feature names that are all strings.

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.

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.

>>> 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_ 3.0. >>> reg.predict(np.array([[3, 5]])) array([16.])

Get metadata routing of this object.

Get parameters for this estimator.

Predict using the linear model.

Return the coefficient of determination of the prediction.

Request metadata passed to the fit method.

Set the parameters of this estimator.

Request metadata passed to the score method.

Parameters : X of shape (n_samples, n_features)

y array-like of shape (n_samples,) or (n_samples, n_targets)

Target values. Will be cast to X’s dtype if necessary.

sample_weight array-like of shape (n_samples,), default=None

Individual weights for each sample.

New in version 0.17: parameter sample_weight support to LinearRegression.

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns : routing MetadataRequest

A MetadataRequest encapsulating routing information.

Get parameters for this estimator.

Parameters : deep bool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns : params dict

Parameter names mapped to their values.

Predict using the linear model.

Parameters : X array-like or sparse matrix, shape (n_samples, n_features)

Returns : C array, shape (n_samples,)

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 — \frac)\) , 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 : X array-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_fitted is the number of samples used in the fitting for the estimator.

y array-like of shape (n_samples,) or (n_samples, n_outputs)

sample_weight array-like of shape (n_samples,), default=None

Returns : score float

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 ).

Request metadata passed to the fit method.

Note that this method is only relevant if enable_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 to fit if provided. The request is ignored if metadata is not provided.
False : metadata is not requested and the meta-estimator will not pass it to fit .
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.

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a pipeline.Pipeline . Otherwise it has no effect.

Parameters : sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit .

Returns : self 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 __ so that it’s possible to update each component of a nested object.

Parameters : **params dict

Returns : self estimator instance