Generalized Linear Model with a Poisson distribution.
This regressor uses the ‘log’ link function.
Read more in the User Guide.
New in version 0.23.
Parameters:alphafloat, default=1Constant 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]. Values must be in the range [0.0, inf]
.
Specifies if a constant [a.k.a. bias or intercept] should be added to the linear predictor [X @ coef + intercept].
max_iterint, default=100The maximal number of iterations for the solver. Values must be in the range [1, inf]
.
Stopping criterion. For the lbfgs solver, the iteration will stop when max{|g_j|, j = 1, ..., d} >> from sklearn import linear_model
>>> clf = linear_model.PoissonRegressor[]
>>> X = [[1, 2], [2, 3], [3, 4], [4, 3]]
>>> y = [12, 17, 22, 21]
>>> clf.fit[X, y]
PoissonRegressor[]
>>> clf.score[X, y]
0.990...
>>> clf.coef_
array[[0.121..., 0.158...]]
>>> clf.intercept_
2.088...
>>> clf.predict[[[1, 1], [3, 4]]]
array[[10.676..., 21.875...]]
Methods
| Fit a Generalized Linear Model. |
| Get parameters for this estimator. |
| Predict using GLM with feature matrix X. |
| Compute D^2, the percentage of deviance explained. |
| Set the parameters of this estimator. |
DEPRECATED: Attribute family
was deprecated in version 1.1 and will be removed in 1.3.
Ensure backward compatibility for the time of deprecation.
fit[X, y, sample_weight=None][source]¶Fit a Generalized Linear Model.
Training data.
yarray-like of shape [n_samples,]Target values.
sample_weightarray-like of shape [n_samples,], default=NoneSample weights.
Returns:selfobjectFitted model.
get_params[deep=True][source]¶Get parameters for this estimator.
Parameters:deepbool, default=TrueIf True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:paramsdictParameter 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=NoneSample weights.
Returns:scorefloatD^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 __
so that it’s possible to update each component of a nested object.
Estimator parameters.
Returns:selfestimator instanceEstimator instance.