Akaike Information Criterion (AIC) for the fitted model.
Standard error of estimated coefficients and intercept.
Degrees of freedom.
The deviance for the fitted model.
The dispersion of the fitted model.
Private copy of model to ensure Params are not modified outside this class.
The deviance for the null model.
Number of instances in DataFrame predictions.
number of iterations
Two-sided p-value of estimated coefficients and intercept.
Field in "predictions" which gives the predicted value of each instance.
Predictions output by the model's
The numeric rank of the fitted linear model.
The residual degrees of freedom.
The residual degrees of freedom for the null model.
Get the residuals of the fitted model by type.
The type of residuals which should be returned. Supported options: deviance, pearson, working and response.
Get the default residuals (deviance residuals) of the fitted model.
the solver algorithm used for model training
T-statistic of estimated coefficients and intercept.