sklearn.dummy.DummyRegressor

class sklearn.dummy.DummyRegressor

DummyRegressor is a regressor that always predicts the mean of the training targets.

This regressor is useful as a simple baseline to compare with other (real) regressors. Do not use it for real problems.

Attributes

y_mean_	float or array of shape [n_outputs]	Mean of the training targets.
n_outputs_	int,	Number of outputs.
outputs_2d_	bool,	True if the output at fit is 2d, else false.

Methods

fit(X, y)	Fit the random regressor.
get_params([deep])	Get parameters for this estimator.
predict(X)	Perform classification on test vectors X.
score(X, y)	Returns the coefficient of determination R^2 of the prediction.
set_params(**params)	Set the parameters of this estimator.

__init__()

x.__init__(...) initializes x; see help(type(x)) for signature

fit(X, y)

Fit the random regressor.

Parameters :

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

Training vectors, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape = [n_samples] or [n_samples, n_outputs]

Target values.

Returns :

self : object

Returns self.

get_params(deep=True)

Get parameters for this estimator.

Parameters :

deep: boolean, optional :

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

Returns :

params : mapping of string to any

Parameter names mapped to their values.

predict(X)

Perform classification on test vectors X.

Parameters :

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

Input vectors, where n_samples is the number of samples and n_features is the number of features.

Returns :

y : array, shape = [n_samples] or [n_samples, n_outputs]

Predicted target values for 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_true - 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)

Test samples.

y : array-like, shape = (n_samples,)

True values for X.

Returns :

score : float

R^2 of self.predict(X) wrt. y.

set_params(**params)

Set the parameters of this 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 :