Utilizing Target Encoding Using Nested CV in Scikit-Learn Pipeline

The Challenge of Data Leakage : Nested Cross-Validation (CV)

Practical Considerations and Best Practices

Implementing target encoding in a pipeline while leveraging nested CV requires careful design to avoid data leakage. Scikit-Learn’s Pipeline and FeatureUnion can be used in conjunction with custom transformers to ensure proper target encoding with following steps:

Create a Custom Transformer for Target Encoding: This transformer should handle the fitting and transformation of target encoding.
Integrate the Transformer in a Pipeline: Include the custom transformer in a Scikit-Learn pipeline.
Apply Nested Cross-Validation: Use nested CV to evaluate the model within the pipeline.

Let’s walk through a step-by-step implementation of target encoding using nested cross-validation within an Sklearn pipeline.

Step 1: Import Necessary Libraries and Create a Sample Dataset

Python

import numpy as np
import pandas as pd
from sklearn.model_selection import KFold, cross_val_score, GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from category_encoders import TargetEncoder

# Sample dataset
data = {
    'category': ['A', 'B', 'A', 'C', 'B', 'A', 'C', 'C', 'B', 'A'],
    'feature': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'target': [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
}
df = pd.DataFrame(data)
X = df[['category', 'feature']]
y = df['target']

Step 2: Define the Pipeline

We will create a pipeline that includes target encoding and a classifier.An Sklearn pipeline is defined, which includes:

TargetEncoder for target encoding the category feature.
StandardScaler for scaling the numerical feature.
RandomForestClassifier as the classifier.

Python

pipeline = Pipeline([
    ('target_encoder', TargetEncoder(cols=['category'])),
    ('scaler', StandardScaler()),
    ('classifier', RandomForestClassifier())
])

Step 3: Nested Cross-Validation

We will use nested cross-validation to evaluate the model. The outer loop will handle the model evaluation, while the inner loop will handle hyperparameter tuning and target encoding. The outer and inner cross-validation strategies are defined using KFold. A parameter grid is defined for hyperparameter tuning of the RandomForestClassifier.

Python

# Define the outer and inner cross-validation strategies
outer_cv = KFold(n_splits=5, shuffle=True, random_state=42)
inner_cv = KFold(n_splits=3, shuffle=True, random_state=42)

# Define the parameter grid for hyperparameter tuning
param_grid = {
    'classifier__n_estimators': [50, 100],
    'classifier__max_depth': [None, 10, 20]
}

# Perform nested cross-validation
grid_search = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=inner_cv, scoring='accuracy')
nested_scores = cross_val_score(grid_search, X, y, cv=outer_cv, scoring='accuracy')
print(f'Nested CV Accuracy: {np.mean(nested_scores):.4f} ± {np.std(nested_scores):.4f}')

Output:

Nested CV Accuracy: 0.1000 ± 0.2000

A nested cross-validation accuracy of 0.1000 ± 0.2000 indicates that the model’s performance is not reliable.

The mean accuracy of 0.1000 suggests that, on average, the model is correctly predicting the target class for only 10% of the samples.
However, the large standard deviation of 0.2000 indicates high variability in model performance across different folds or iterations of cross-validation.

Target encoding using nested CV in sklearn pipeline

In machine learning, feature engineering plays a pivotal role in enhancing model performance. One such technique is target encoding, which is particularly useful for categorical variables. However, improper implementation can lead to data leakage and overfitting. This article delves into the intricacies of target encoding using nested cross-validation (CV) within an Sklearn pipeline, ensuring a robust and unbiased model evaluation.

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