DummyRegressor (original) (raw)

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

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.

Added in version 0.13.

Parameters:

strategy{“mean”, “median”, “quantile”, “constant”}, default=”mean”

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.

constantint or float or array-like of shape (n_outputs,), default=None

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

quantilefloat in [0.0, 1.0], default=None

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_**ndarray of shape (1, n_outputs)

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

**n_features_in_**int

Number of features seen during fit.

**feature_names_in_**ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

**n_outputs_**int

Number of outputs.

See also

DummyClassifier

Classifier that makes predictions using simple rules.

Examples

import numpy as np from sklearn.dummy import DummyRegressor X = np.array([1.0, 2.0, 3.0, 4.0]) y = np.array([2.0, 3.0, 5.0, 10.0]) dummy_regr = DummyRegressor(strategy="mean") dummy_regr.fit(X, y) DummyRegressor() dummy_regr.predict(X) array([5., 5., 5., 5.]) dummy_regr.score(X, y) 0.0

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

Fit the baseline regressor.

Parameters:

Xarray-like of shape (n_samples, n_features)

Training data.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

Target values.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

selfobject

Fitted estimator.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True

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

Returns:

paramsdict

Parameter names mapped to their values.

predict(X, return_std=False)[source]#

Perform classification on test vectors X.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test data.

return_stdbool, default=False

Whether to return the standard deviation of posterior prediction. All zeros in this case.

Added in version 0.20.

Returns:

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

Predicted target values for X.

y_stdarray-like of shape (n_samples,) or (n_samples, n_outputs)

Standard deviation of predictive distribution of query points.

score(X, y, sample_weight=None)[source]#

Return 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:

XNone or array-like of shape (n_samples, n_features)

Test samples. Passing None as test samples gives the same result as passing real test samples, since DummyRegressoroperates independently of the sampled observations.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

R^2 of self.predict(X) w.r.t. y.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → DummyRegressor[source]#

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
• False: metadata is not requested and the meta-estimator will not pass it to fit.
• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns:

selfobject

The updated object.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_predict_request(*, return_std: bool | None | str = '$UNCHANGED$') → DummyRegressor[source]#

Configure whether metadata should be requested to be passed to the predict method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.
• False: metadata is not requested and the meta-estimator will not pass it to predict.
• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

return_stdstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for return_std parameter in predict.

Returns:

selfobject

The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → DummyRegressor[source]#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
• False: metadata is not requested and the meta-estimator will not pass it to score.
• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:

selfobject

The updated object.

Gallery examples#