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# `sklearn.dummy`.DummyRegressor

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

  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.

    constant int 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.

    quantile float 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.

  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_metadata_routing`() | Get metadata routing of this object.
  `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_fit_request`(*[, sample_weight]) | Request metadata passed to the `fit` method.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_predict_request`(*[, return_std]) | Request metadata passed to the `predict` method.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.

  fit(X, y, sample_weight=None)

    Fit the random regressor.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Training data.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        Target values.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights.

    Returns:
      self object

        Fitted estimator.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

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

    Returns:
      params dict

        Parameter names mapped to their values.

  predict(X, return_std=False)

    Perform classification on test vectors X.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Test data.

      return_std bool, default=False

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

        New in version 0.20.

    Returns:
      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        Predicted target values for X.

      y_std array-like of shape (n_samples,) or (n_samples, n_outputs)

        Standard deviation of predictive distribution of query points.

  score(X, y, sample_weight=None)

    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:
      X None 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.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        True values for X.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights.

    Returns:
      score float

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

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

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see 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.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_params(**params)

    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:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

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

    Request metadata passed to the `predict` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see 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.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      return_std str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `return_std` parameter in `predict`.

    Returns:
      self object

        The updated object.

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

    Request metadata passed to the `score` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see 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.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.

## Examples using `sklearn.dummy.DummyRegressor`

Poisson regression and non-normal loss

Poisson regression and non-normal loss

Tweedie regression on insurance claims

Tweedie regression on insurance claims