8.14.1.1. sklearn.linear_model.LinearRegression¶
- class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True)¶
Ordinary least squares Linear Regression.
Parameters : 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
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
decision_function(X) Decision function of the linear model fit(X, y) Fit linear model. predict(X) Predict using the linear model score(X, y) Returns the coefficient of determination R^2 of the prediction. set_params(**params) Set the parameters of the estimator. - __init__(fit_intercept=True, normalize=False, copy_X=True)¶
- decision_function(X)¶
Decision function of the linear model
Parameters : X : numpy array of shape [n_samples, n_features]
Returns : C : array, shape = [n_samples]
Returns predicted values.
- fit(X, y)¶
Fit linear model.
Parameters : X : numpy array or sparse matrix of shape [n_samples,n_features]
Training data
y : numpy array of shape [n_samples]
Target values
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 R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0, lower values are worse.
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 :