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.
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. - fit(X, y, sample_weight=None)[source]¶
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)[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.
Returns: params : mapping of string to any
Parameter names mapped to their values.
- predict(X)[source]¶
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)[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 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)[source]¶
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 :