Note

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# 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:

numerical-numerical: Pearson’s correlation coefficient

numerical-categorical: Correlation ratio

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')
```

## Run the check#

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

**Total running time of the script:** ( 0 minutes 0.102 seconds)