The feature_importance method returns two dataframes that contain feature importance metrics that depend on the types of the feature/target
| Feature/Task | Classification | Regression |
|---|---|---|
| Categorical | Chi²-test + Cramér's V | F-test |
| Numerical | F-test | Pearson correlation |
Additionally mutual information can be used in each case.
Classification.
>>> import pandas as pd
>>> from sklearn import datasets
>>> import xam
>>> iris = datasets.load_iris()
>>> features = pd.DataFrame(iris.data, columns=iris.feature_names)
>>> features['sepal length (cm)'] = features['sepal length (cm)'] > 5.5
>>> target = pd.Series(iris.target)
>>> cont_imp, disc_imp = xam.eda.feature_importance_classification(features, target, random_state=1)
>>> cont_imp.sort_values('f_p_value') # doctest: +SKIP
f_statistic f_p_value mutual_information
petal length (cm) 1179.034328 3.051976e-91 0.990061
petal width (cm) 959.324406 4.376957e-85 0.977279
sepal width (cm) 47.364461 1.327917e-16 0.256295
>>> disc_imp.sort_values('chi2_p_value') # doctest: +SKIP
chi2_statistic chi2_p_value cramers_v mutual_information
sepal length (cm) 98.11883 4.940452e-22 0.803139 0.386244Regression.
>>> import pandas as pd
>>> from sklearn import datasets
>>> import xam
>>> boston = datasets.load_boston()
>>> features = pd.DataFrame(boston.data, columns=boston.feature_names)
>>> features['CHAS'] = features['CHAS'].astype(int)
>>> target = pd.Series(boston.target)
>>> cont_imp, disc_imp = xam.eda.feature_importance_regression(features, target, random_state=1)
>>> cont_imp.sort_values('pearson_r_p_value')
pearson_r pearson_r_p_value mutual_information
LSTAT -0.737663 5.081103e-88 0.666882
RM 0.695360 2.487229e-74 0.526456
PTRATIO -0.507787 1.609509e-34 0.453291
INDUS -0.483725 4.900260e-31 0.471507
TAX -0.468536 5.637734e-29 0.363694
NOX -0.427321 7.065042e-24 0.456947
CRIM -0.388305 1.173987e-19 0.339019
RAD -0.381626 5.465933e-19 0.217623
AGE -0.376955 1.569982e-18 0.311285
ZN 0.360445 5.713584e-17 0.195153
B 0.333461 1.318113e-14 0.161861
DIS 0.249929 1.206612e-08 0.295207
>>> disc_imp.sort_values('mutual_information')
f_statistic f_p_value mutual_information
CHAS 15.971512 0.000074 0.030825