sklearn.dummy.DummyRegressor

class sklearn.dummy.DummyRegressor(strategy='mean', constant=None, quantile=None)

DummyRegressor is a regressor that makes predictions using simple rules.

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

Read more in the User Guide.

Parameters:

strategy : str

Strategy to use to generate predictions.

• “mean”: always predicts the mean of the training set
• “median”: always predicts the median of the training set
• “quantile”: always predicts a specified quantile of the training set, provided with the quantile parameter.
• “constant”: always predicts a constant value that is provided by the user.

constant : int or float or array of shape = [n_outputs]

The explicit constant as predicted by the “constant” strategy. This parameter is useful only for the “constant” strategy.

quantile : float in [0.0, 1.0]

The quantile to predict using the “quantile” strategy. A quantile of 0.5 corresponds to the median, while 0.0 to the minimum and 1.0 to the maximum.

Attributes:

constant_ : float or array of shape [n_outputs]

Mean or median or quantile of the training targets or constant value given by the user.

n_outputs_ : int,

Number of outputs.

outputs_2d_ : bool,

True if the output at fit is 2d, else false.

Methods

fit(X, y[, sample_weight])	Fit the random regressor.
get_params([deep])	Get parameters for this estimator.
predict(X)	Perform classification on test vectors X.
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__(strategy='mean', constant=None, quantile=None)

fit(X, y, sample_weight=None)

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.

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

Sample weights.

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, sample_weight=None)

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 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.

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.

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 latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :