• API Reference - FeatureLabelCorrelation

Feature Label Correlation

This notebook provides an overview for using and understanding the Feature Label Correlation check.

Structure:

• What is Feature Label Correlation

• Generate data

• Run the check

What is Feature Label Correlation

The FeatureLabelCorrelation check computes the correlation between each feature and the label, potentially spotting features highly correlated with the label.

This check works with 2 types of columns: categorical and numerical, and uses a different method to calculate the correlation for each feature label pair:

1. numerical-numerical: Pearson’s correlation coefficient

2. numerical-categorical: Correlation ratio

3. categorical-categorical: Symmetric Theil’s U

Imports

import numpy as np
import pandas as pd

from deepchecks.tabular import Dataset
from deepchecks.tabular.checks import FeatureLabelCorrelation

Generate 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=[])

Run the check

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

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Feature Feature Correlation

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Identifier Label Correlation