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# `sklearn.linear_model`.GammaRegressor

class sklearn.linear_model.GammaRegressor(*, alpha=1.0, fit_intercept=True, solver='lbfgs', max_iter=100, tol=0.0001, warm_start=False, verbose=0)

  Generalized Linear Model with a Gamma distribution.

  This regressor uses the ‘log’ link function.

  Read more in the User Guide.

  New in version 0.23.

  Parameters:
    alpha float, default=1

      Constant that multiplies the L2 penalty term and determines the regularization strength. `alpha = 0` is equivalent to unpenalized GLMs. In this case, the design matrix `X` must have full column rank (no collinearities). Values of `alpha` must be in the range `[0.0, inf)`.

    fit_intercept bool, default=True

      Specifies if a constant (a.k.a. bias or intercept) should be added to the linear predictor `X @ coef_ + intercept_`.

    solver{‘lbfgs’, ‘newton-cholesky’}, default=’lbfgs’

      Algorithm to use in the optimization problem:

      ‘lbfgs’

        Calls scipy’s L-BFGS-B optimizer.

      ‘newton-cholesky’

        Uses Newton-Raphson steps (in arbitrary precision arithmetic equivalent to iterated reweighted least squares) with an inner Cholesky based solver. This solver is a good choice for `n_samples` >> `n_features`, especially with one-hot encoded categorical features with rare categories. Be aware that the memory usage of this solver has a quadratic dependency on `n_features` because it explicitly computes the Hessian matrix.

        New in version 1.2.

    max_iter int, default=100

      The maximal number of iterations for the solver. Values must be in the range `[1, inf)`.

    tol float, default=1e-4

      Stopping criterion. For the lbfgs solver, the iteration will stop when `max{|g_j|, j = 1, ..., d} <= tol` where `g_j` is the j-th component of the gradient (derivative) of the objective function. Values must be in the range `(0.0, inf)`.

    warm_start bool, default=False

      If set to `True`, reuse the solution of the previous call to `fit` as initialization for `coef_` and `intercept_`.

    verbose int, default=0

      For the lbfgs solver set verbose to any positive number for verbosity. Values must be in the range `[0, inf)`.

  Attributes:
    coef_array of shape (n_features,)

      Estimated coefficients for the linear predictor (`X @ coef_ + intercept_`) in the GLM.

    intercept_float

      Intercept (a.k.a. bias) added to linear predictor.

    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    n_iter_int

      Actual number of iterations used in the solver.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

  See also

  `PoissonRegressor`

    Generalized Linear Model with a Poisson distribution.

  `TweedieRegressor`

    Generalized Linear Model with a Tweedie distribution.

  Examples

      >>> from sklearn import linear_model
      >>> clf = linear_model.GammaRegressor()
      >>> X = [[1, 2], [2, 3], [3, 4], [4, 3]]
      >>> y = [19, 26, 33, 30]
      >>> clf.fit(X, y)
      GammaRegressor()
      >>> clf.score(X, y)
      0.773...
      >>> clf.coef_
      array([0.072..., 0.066...])
      >>> clf.intercept_
      2.896...
      >>> clf.predict([[1, 0], [2, 8]])
      array([19.483..., 35.795...])

  Methods

  `fit`(X, y[, sample_weight]) | Fit a Generalized Linear Model.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `predict`(X) | Predict using GLM with feature matrix X.
  `score`(X, y[, sample_weight]) | Compute D^2, the percentage of deviance explained.
  `set_fit_request`(*[, sample_weight]) | Request metadata passed to the `fit` method.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.

  fit(X, y, sample_weight=None)

    Fit a Generalized Linear Model.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Training data.

      y array-like of shape (n_samples,)

        Target values.

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

        Sample weights.

    Returns:
      self object

        Fitted model.

  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)

    Predict using GLM with feature matrix X.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Samples.

    Returns:
      y_pred array of shape (n_samples,)

        Returns predicted values.

  score(X, y, sample_weight=None)

    Compute D^2, the percentage of deviance explained.

    D^2 is a generalization of the coefficient of determination R^2. R^2 uses squared error and D^2 uses the deviance of this GLM, see the User Guide.

    D^2 is defined as \(D^2 = 1-\frac{D(y_{true},y_{pred})}{D_{null}}\), \(D_{null}\) is the null deviance, i.e. the deviance of a model with intercept alone, which corresponds to \(y_{pred} = \bar{y}\). The mean \(\bar{y}\) is averaged by sample_weight. Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Test samples.

      y array-like of shape (n_samples,)

        True values of target.

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

        Sample weights.

    Returns:
      score float

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

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

    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_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → GammaRegressor

    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.linear_model.GammaRegressor`

Release Highlights for scikit-learn 0.23

Release Highlights for scikit-learn 0.23

Tweedie regression on insurance claims

Tweedie regression on insurance claims