8.15.2.4. sklearn.linear_model.sparse.SGDRegressor

class sklearn.linear_model.sparse.SGDRegressor(*args, **kwargs)

Methods

decision_function(X)	Predict using the linear model
fit(X, y[, coef_init, intercept_init, ...])	Fit linear model with Stochastic Gradient Descent.
fit_transform(X[, y])	Fit to data, then transform it
get_params([deep])	Get parameters for the estimator
partial_fit(X, y[, sample_weight])	Fit linear model with Stochastic Gradient Descent.
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.
transform(X[, threshold])	Reduce X to its most important features.

__init__(*args, **kwargs)

DEPRECATED: to be removed in v0.12; use sklearn.linear_model.SGDRegressor directly

decision_function(X)

Predict using the linear model

Parameters :

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Returns :

array, shape = [n_samples] :

Predicted target values per element in X.

fit(X, y, coef_init=None, intercept_init=None, sample_weight=None)

Fit linear model with Stochastic Gradient Descent.

Parameters :

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Training data

y : numpy array of shape [n_samples]

Target values

coef_init : array, shape = [n_features]

The initial coeffients to warm-start the optimization.

intercept_init : array, shape = [1]

The initial intercept to warm-start the optimization.

sample_weight : array-like, shape = [n_samples], optional

Weights applied to individual samples (1. for unweighted).

Returns :

self : returns an instance of self.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters :

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns :

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

Notes

This method just calls fit and transform consecutively, i.e., it is not an optimized implementation of fit_transform, unlike other transformers such as PCA.

get_params(deep=True)

Get parameters for the estimator

Parameters :

deep: boolean, optional :

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

partial_fit(X, y, sample_weight=None)

Fit linear model with Stochastic Gradient Descent.

Parameters :

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Subset of training data

y : numpy array of shape [n_samples]

Subset of target values

sample_weight : array-like, shape = [n_samples], optional

Weights applied to individual samples. If not provided, uniform weights are assumed.

Returns :

self : returns an instance of self.

predict(X)

Predict using the linear model

Parameters :

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Returns :

array, shape = [n_samples] :

Predicted target values per element in X.

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 :

transform(X, threshold=None)

Reduce X to its most important features.

Parameters :

X : array or scipy sparse matrix of shape [n_samples, n_features]

The input samples.

threshold : string, float or None, optional (default=None)

The threshold value to use for feature selection. Features whose importance is greater or equal are kept while the others are discarded. If “median” (resp. “mean”), then the threshold value is the median (resp. the mean) of the feature importances. A scaling factor (e.g., “1.25*mean”) may also be used. If None and if available, the object attribute threshold is used. Otherwise, “mean” is used by default.

Returns :

X_r : array of shape [n_samples, n_selected_features]

The input samples with only the selected features.