sklearn.linear_model.PoissonRegressor

class sklearn.linear_model.PoissonRegressor(*, alpha=1.0, fit_intercept=True, solver='lbfgs', 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 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).

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_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

TweedieRegressor

Generalized Linear Model with a Tweedie 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

Ensure backward compatibility for the time of deprecation.

Deprecated since version 1.1: Will be removed in 1.3

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 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_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

Release Highlights for scikit-learn 0.23

Poisson regression and non-normal loss

Poisson regression and non-normal loss

Tweedie regression on insurance claims

Tweedie regression on insurance claims