Feature Feature Correlation#
This notebook provides an overview for using and understanding the feature-feature correlation check.
This check computes the pairwise correlations between the features, potentially spotting pairs of features that are highly correlated.
How are The Correlations Calculated?#
This check works with 2 types of features: categorical and numerical, and uses a different method to calculate the correlation for each combination of feature types:
import pandas as pd from deepchecks.tabular.datasets.classification import adult from deepchecks.tabular.checks.data_integrity import FeatureFeatureCorrelation
We load the Adult dataset, a dataset based on the 1994 US Census containing both numerical and categorical features.
Run the Check#
Define a Condition#
Now we will define a condition on the maximum number of pairs that are correlated above a certain threshold. In this example, we will define a condition that the maximum number of pairs that are correlated above 0.8 is less than 3.
Total running time of the script: ( 0 minutes 2.631 seconds)