TweedieRegressor (original) (raw)
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 thepower parameter, which determines the underlying distribution.
Read more in the User Guide.
Added in version 0.23.
Parameters:
powerfloat, default=0
The power determines the underlying target distribution according to the following table:
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 predictorX @ 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 onn_features because it explicitly computes the Hessian matrix.
Added 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} <= tolwhere 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 fitas 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.
Added in version 0.24.
**feature_names_in_**ndarray of shape (n_features_in_,)
Names of features seen during fit. Defined only when Xhas feature names that are all strings.
Added in version 1.0.
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) np.float64(0.839...) clf.coef_ array([0.599..., 0.299...]) clf.intercept_ np.float64(1.600...) clf.predict([[1, 1], [3, 4]]) array([2.500..., 4.599...])
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 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 theUser 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 ifenable_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 tofitif provided. The request is ignored if metadata is not provided.False: metadata is not requested and the meta-estimator will not pass it tofit.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.
Added 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 aPipeline. 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 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 ifenable_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 toscoreif provided. The request is ignored if metadata is not provided.False: metadata is not requested and the meta-estimator will not pass it toscore.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.
Added 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 aPipeline. 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.