sklearn.dummy
.DummyRegressor¶
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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[, 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.
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fit
(X, y, sample_weight=None)[source]¶ Fit the random regressor.
Parameters: - X : {array-like, object with finite length or shape}
Training data, requires length = n_samples
- 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
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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.
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predict
(X, return_std=False)[source]¶ Perform classification on test vectors X.
Parameters: - X : {array-like, object with finite length or shape}
Training data, requires length = n_samples
- return_std : boolean, optional
Whether to return the standard deviation of posterior prediction. All zeros in this case.
Returns: - y : array, shape = [n_samples] or [n_samples, n_outputs]
Predicted target values for X.
- y_std : array, shape = [n_samples] or [n_samples, n_outputs]
Standard deviation of predictive distribution of query points.
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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 : array-like, shape = (n_samples, n_features)
Test samples. 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.
- 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.
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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.Returns: - self