sklearn.dummy
.DummyRegressor¶
- 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.
New 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_
None
DEPRECATED:
n_features_in_
is deprecated in 1.0 and will be removed in 1.2.- 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
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])Return 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
- 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_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.
- property n_features_in_¶
DEPRECATED:
n_features_in_
is deprecated in 1.0 and will be removed in 1.2.
- 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.
New 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)
, whereu
is the residual sum of squares((y_true - y_pred) ** 2).sum()
andv
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
DummyRegressor
operates 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)
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
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.