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9.2.1. sklearn.linear_model.LinearRegression

class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, overwrite_X=False)

Ordinary least squares Linear Regression.

Notes

From the implementation point of view, this is just plain Ordinary Least Squares (numpy.linalg.lstsq) wrapped as a predictor object.

Attributes

coef_ array Estimated coefficients for the linear regression problem.
intercept_ array Independent term in the linear model.

Methods

fit
predict
score
set_params
__init__(fit_intercept=True, normalize=False, overwrite_X=False)
fit(X, y)

Fit linear model.

Parameters :

X : numpy array of shape [n_samples,n_features]

Training data

y : numpy array of shape [n_samples]

Target values

fit_intercept : boolean, optional

wether 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).

normalize : boolean, optional

If True, the regressors X are normalized

Returns :

self : returns an instance of self.

predict(X)

Predict using the linear model

Parameters :

X : numpy array of shape [n_samples, n_features]

Returns :

C : array, shape = [n_samples]

Returns predicted values.

score(X, y)

Returns the coefficient of determination of the prediction

Parameters :

X : array-like, shape = [n_samples, n_features]

Training set.

y : array-like, shape = [n_samples]

Returns :

z : float

set_params(**params)

Set the parameters of the estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :