sklearn.dummy.DummyRegressor¶

class sklearn.dummy.DummyRegressor(strategy=’mean’, constant=None, quantile=None)[source]

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. 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[, return_std]) 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)[source]
fit(X, y, sample_weight=None)[source]

Fit the random regressor.

Parameters: X : {array-like, object with finite length or shape} Training data, requires length = n_samples y : array-like, shape = [n_samples] or [n_samples, n_outputs] Target values. sample_weight : array-like of shape = [n_samples], optional Sample weights. self : object
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. params : mapping of string to any Parameter names mapped to their values.
predict(X, return_std=False)[source]

Perform classification on test vectors X.

Parameters: X : {array-like, object with finite length or shape} Training data, requires length = n_samples return_std : boolean, optional Whether to return the standard deviation of posterior prediction. All zeros in this case. y : array, shape = [n_samples] or [n_samples, n_outputs] Predicted target values for X. y_std : array, shape = [n_samples] or [n_samples, n_outputs] Standard deviation of predictive distribution of query points.
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, None} Test samples with shape = (n_samples, n_features) or None. 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. Passing None as test samples gives the same result as passing real test samples, since DummyRegressor operates independently of the sampled observations. 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. score : float R^2 of self.predict(X) wrt. y.
set_params(**params)[source]

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