Feature Label Correlation#

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

Structure:

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)
Feature Label Correlation


Total running time of the script: ( 0 minutes 0.114 seconds)

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