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.
New in version 0.13.
 Parameters
 strategystr
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 arraylike of shape (n_outputs,)
The explicit constant as predicted by the “constant” strategy. This parameter is useful only for the “constant” strategy.
 quantilefloat 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_array, shape (1, n_outputs)
Mean or median or quantile of the training targets or constant value given by the user.
 n_outputs_int,
Number of outputs.
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
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]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(X, y, sample_weight=None)[source]¶ Fit the random regressor.
 Parameters
 X{arraylike, object with finite length or shape}
Training data, requires length = n_samples
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
Target values.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 selfobject

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
 paramsmapping of string to any
Parameter names mapped to their values.

predict
(X, return_std=False)[source]¶ Perform classification on test vectors X.
 Parameters
 X{arraylike, object with finite length or shape}
Training data, requires length = n_samples
 return_stdboolean, optional
Whether to return the standard deviation of posterior prediction. All zeros in this case.
New in version 0.20.
 Returns
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
Predicted target values for X.
 y_stdarraylike of 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{arraylike, 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.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
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. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.