Feature Engineering for Machine Learning

16.

Common Pitfalls and Solutions

16.1.

Data Leakage Issues

16.1.1.

Look-Ahead Bias

16.1.2.

Information Leakage

16.1.3.

Temporal Violations

16.2.

Overfitting in Feature Engineering

16.2.1.

Feature Selection Overfitting

16.2.2.

Transformation Overfitting

16.2.3.

Validation Set Contamination

16.3.

Scalability Problems

16.3.1.

High-Dimensional Features

16.3.2.

Computational Complexity

16.3.3.

Memory Constraints

16.4.

Domain Knowledge Integration

16.4.1.

Expert Input Incorporation

16.4.2.

Business Logic Validation

16.4.3.

Feature Interpretability

16.5.

Maintenance and Updates

16.5.1.

Feature Drift Handling

16.5.2.

Schema Evolution

16.5.3.

Backward Compatibility

Previous

15. Implementation and Best Practices

Go to top

Back to Start

1. Introduction to Feature Engineering