sklearn.linear_model.PoissonRegressor

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

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=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_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.

n_iter_int

Actual number of iterations used in the solver.

See also

GeneralizedLinearRegressor

Generalized Linear Model with a Poisson distribution.

Examples

>>> 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(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.

property family

Return the string 'poisson'.

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.

yarray-like of shape (n_samples,)

Target values.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns

selfobject

Fitted model.

get_params(deep=True)

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)

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)

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)

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.PoissonRegressor

Release Highlights for scikit-learn 0.23

Poisson regression and non-normal loss

Tweedie regression on insurance claims