`sklearn.linear_model`.TweedieRegressor#

class sklearn.linear_model.TweedieRegressor(*, power=0.0, alpha=1.0, fit_intercept=True, link='auto', solver='lbfgs', max_iter=100, tol=0.0001, warm_start=False, verbose=0)[source]#

Generalized Linear Model with a Tweedie distribution.

This estimator can be used to model different GLMs depending on the power parameter, which determines the underlying distribution.

Read more in the User Guide.

New in version 0.23.

Parameters:

powerfloat, default=0

The power determines the underlying target distribution according to the following table:

Power	Distribution
0	Normal
1	Poisson
(1,2)	Compound Poisson Gamma
2	Gamma
3	Inverse Gaussian

For 0 < power < 1, no distribution exists.

alphafloat, 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_interceptbool, default=True

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

link{‘auto’, ‘identity’, ‘log’}, default=’auto’

The link function of the GLM, i.e. mapping from linear predictor X @ coeff + intercept to prediction y_pred. Option ‘auto’ sets the link depending on the chosen power parameter as follows:

‘identity’ for power <= 0, e.g. for the Normal distribution
‘log’ for power > 0, e.g. for Poisson, Gamma and Inverse Gaussian distributions

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_iterint, default=100

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

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. Values must be in the range (0.0, inf).

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. 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_iter_int: Actual number of iterations used in the solver.
n_features_in_int: Number of features seen during fit.

New in version 0.24.
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.
GammaRegressor: Generalized Linear Model with a Gamma distribution.

Examples

>>> from sklearn import linear_model
>>> clf = linear_model.TweedieRegressor()
>>> X = [[1, 2], [2, 3], [3, 4], [4, 3]]
>>> y = [2, 3.5, 5, 5.5]
>>> clf.fit(X, y)
TweedieRegressor()
>>> clf.score(X, y)
0.839...
>>> clf.coef_
array([0.599..., 0.299...])
>>> clf.intercept_
1.600...
>>> clf.predict([[1, 1], [3, 4]])
array([2.500..., 4.599...])

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)[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:

selfobject: Fitted model.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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 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.
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_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TweedieRegressor[source]#

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_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

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.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TweedieRegressor[source]#

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_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

Examples using `sklearn.linear_model.TweedieRegressor`#

Release Highlights for scikit-learn 0.23

Tweedie regression on insurance claims

sklearn.linear_model.TweedieRegressor#

Examples using sklearn.linear_model.TweedieRegressor#

`sklearn.linear_model`.TweedieRegressor#

Examples using `sklearn.linear_model.TweedieRegressor`#