pipefitter.estimator.LogisticRegression¶
-
class
pipefitter.estimator.LogisticRegression(intercept=True, max_effects=0, selection='none', sig_level=0.05, criterion=None, target=None, nominals=None, inputs=None)¶ Bases:
pipefitter.base.BaseEstimatorLogistic Regression
Parameters: intercept : bool, optional
Include the intercept term in the model?
max_effects : int, optional
Specifies the maximum number of effects in any model to consider during the selection process
selection : string, optional
Specifies the selection method. Valid values are ‘none’, ‘backward’, ‘forward’, and ‘stepwise’.
sig_level : float, optional
Specifies the significance level
criterion : string, optional
Specifies selection criterion. Valid values are ‘sl’, ‘aic’, ‘aicc’, and ‘sbc’.
target : string, optional
The target variable
nominals : string or list of strings, optional
The nominal variables
inputs : string or list of strings, optional
The input variables
Returns: Examples
>>> log = LogisticRegression(target='Origin', ... inputs=['MPG_City', 'MPG_Highway', 'Length', ... 'Weight', 'Type', 'Cylinders'], ... nominals = ['Type', 'Cylinders', 'Origin'])
-
__init__(intercept=True, max_effects=0, selection='none', sig_level=0.05, criterion=None, target=None, nominals=None, inputs=None)¶
Methods
__init__([intercept, max_effects, ...])fit(table, \*args, \*\*kwargs)Fit function for logistic regression get_combined_params(\*args, \*\*kwargs)Merge all parameters and verify that they valid get_filtered_params(\*args, \*\*kwargs)Merge parameters that keys that belong to self get_param(\*names)Return a copy of the requested parameters get_params(\*names)Return a copy of the requested parameters has_param(name)Does the parameter exist? set_param(\*args, \*\*kwargs)Set one or more parameters set_params(\*args, \*\*kwargs)Set one or more parameters transform(table, \*args, \*\*kwargs)Transform function for transformer Attributes
param_defsstatic_params-