Single Feature Contribution#

Imports#

import numpy as np
import pandas as pd

from deepchecks.tabular import Dataset
from deepchecks.tabular.checks.methodology import *


Generating Data#

df = pd.DataFrame(np.random.randn(100, 3), columns=['x1', 'x2', 'x3'])
df['x4'] = df['x1'] * 0.5 + df['x2']
df['label'] = df['x2'] + 0.1 * df['x1']
df['x5'] = df['label'].apply(lambda x: 'v1' if x < 0 else 'v2')

ds = Dataset(df, label='label', cat_features=[])


Running single_feature_contribution check#

SingleFeatureContribution().run(ds)


Single Feature Contribution

Return the PPS (Predictive Power Score) of all features in relation to the label.

The Predictive Power Score (PPS) is used to estimate the ability of a feature to predict the label by itself (Read more about Predictive Power Score). A high PPS (close to 1) can mean that this feature's success in predicting the label is actually due to data leakage - meaning that the feature holds information that is based on the label to begin with.

Using the SingleFeatureContribution check class#

my_check = SingleFeatureContribution(ppscore_params={'sample': 10})
my_check.run(dataset=ds)


Single Feature Contribution

Return the PPS (Predictive Power Score) of all features in relation to the label.