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) [source]
 
Generalized Linear Model with a Poisson distribution. 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_iter_int 

Actual number of iterations used in the solver.     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.    
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 
 
selfreturns an instance of self. 
   
  
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 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) [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.     
 
 Examples using sklearn.linear_model.PoissonRegressor
 
  Release Highlights for scikit-learn 0.23  

  Poisson regression and non-normal loss  

  Tweedie regression on insurance claims