Target encoding using nested CV in sklearn pipeline
What is data leakage, and why is it a problem?
Data leakage occurs when information from outside the training dataset is used to create the model, leading to overly optimistic performance estimates. It is a problem because it means the model may not perform as well on unseen data.
Can target encoding be used for regression tasks?
Yes, target encoding can be adapted for regression tasks by replacing categories with the mean of the target variable.
What are some alternatives to target encoding?
Alternatives include one-hot encoding, frequency encoding, and leave-one-out encoding.
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.
Table of Content
- Understanding Target Encoding
- The Challenge of Data Leakage : Nested Cross-Validation (CV)
- Utilizing Target Encoding Using Nested CV in Scikit-Learn Pipeline
- Practical Considerations and Best Practices
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