`sklearn.linear_model`.GammaRegressor¶

class sklearn.linear_model.GammaRegressor(*, alpha=1.0, fit_intercept=True, max_iter=100, tol=0.0001, warm_start=False, verbose=0)[source]¶

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

alphafloat, default=1: Constant that multiplies the penalty term and thus 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).
fit_interceptbool, default=True: Specifies if a constant (a.k.a. bias or intercept) should be added to the linear predictor (X @ coef + intercept).
max_iterint, default=100: The maximal number of iterations for the solver.
tolfloat, 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.
warm_startbool, default=False: If set to True, reuse the solution of the previous call to fit as initialization for coef_ and intercept_ .
verboseint, default=0: For the lbfgs solver set verbose to any positive number for verbosity.

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_iter_int: Actual number of iterations used in the solver.

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_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_params`(**params)	Set the parameters of this estimator.

fit(X, y, sample_weight=None)[source]¶

Fit a Generalized Linear Model.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features): Training data.
yarray-like of shape (n_samples,): Target values.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns

selfreturns an instance of self.

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)[source]¶

Predict using GLM with feature matrix X.

Parameters

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

Returns

y_predarray of shape (n_samples,): Returns predicted values.

score(X, y, sample_weight=None)[source]¶

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 deviance. Note that those two are equal for family='normal'.

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.
yarray-like of shape (n_samples,): True values of target.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns

scorefloat: D^2 of self.predict(X) w.r.t. 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.linear_model.GammaRegressor`¶

sklearn.linear_model.GammaRegressor¶

Examples using sklearn.linear_model.GammaRegressor¶

`sklearn.linear_model`.GammaRegressor¶

Examples using `sklearn.linear_model.GammaRegressor`¶