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 
 
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_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.    
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 
   
  
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.  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]
 
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 
 
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.     
 
 Examples using sklearn.dummy.DummyRegressor
 
  Poisson regression and non-normal loss