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 :