sklearn.linear_model.LinearRegression — scikit-learn 0.20.4 documentation (original) (raw)
class sklearn.linear_model. LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=None)[source]¶
Ordinary least squares Linear Regression.
| Parameters: | fit_intercept : boolean, optional, default True whether 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, default False This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please usesklearn.preprocessing.StandardScaler before calling fit on an estimator with normalize=False. copy_X : boolean, optional, default True If True, X will be copied; else, it may be overwritten. n_jobs : int or None, optional (default=None) The number of jobs to use for the computation. This will only provide speedup for n_targets > 1 and sufficient large problems.None means 1 unless in a joblib.parallel_backend context.-1 means using all processors. See Glossaryfor more details. |
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| Attributes: | coef_ : array, 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. intercept_ : array Independent term in the linear model. |
Notes
From the implementation point of view, this is just plain Ordinary Least Squares (scipy.linalg.lstsq) 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_ 3.0000... reg.predict(np.array([[3, 5]])) array([16.])
Methods
| fit(X, y[, sample_weight]) | Fit linear model. |
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| get_params([deep]) | Get parameters for this estimator. |
| predict(X) | Predict using the linear model |
| score(X, y[, sample_weight]) | Returns the coefficient of determination R^2 of the prediction. |
| set_params(**params) | Set the parameters of this estimator. |
__init__(fit_intercept=True, normalize=False, copy_X=True, n_jobs=None)[source]¶
fit(X, y, sample_weight=None)[source]¶
Fit linear model.
| Parameters: | X : array-like or sparse matrix, shape (n_samples, n_features) Training data y : array_like, shape (n_samples, n_targets) Target values. Will be cast to X’s dtype if necessary sample_weight : numpy array of shape [n_samples] Individual weights for each sample New in version 0.17: parameter sample_weight support to LinearRegression. |
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| Returns: | self : returns an instance of self. |
get_params(deep=True)[source]¶
Get parameters for this estimator.
| Parameters: | deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. |
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| Returns: | params : mapping of string to any Parameter names mapped to their values. |
Predict using the linear model
| Parameters: | X : array_like or sparse matrix, shape (n_samples, n_features) Samples. |
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| Returns: | C : array, shape (n_samples,) Returns predicted values. |
score(X, y, sample_weight=None)[source]¶
Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - 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: | X : array-like, shape = (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix instead, 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, shape = (n_samples) or (n_samples, n_outputs) True values for X. sample_weight : array-like, shape = [n_samples], optional Sample weights. |
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| Returns: | score : float R^2 of self.predict(X) wrt. y. |
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form<component>__<parameter> so that it’s possible to update each component of a nested object.
| Returns: | self |
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