Machine Learning Interview Question: What is Cross-Validation?3 min readJust now--In this crisp to-the-point interview guide, youll explore:1. What is cross-validation?2. Why is it important?3. Types of cross-validation techniques4. Sample Python Code.5. Common pitfalls to avoid.What is Cross-Validation?Cross-validation is a technique to assess the performance of a machine learning model by splitting the dataset into multiple training and validation sets.The goal is to ensure the model generalizes well to unseen data and avoid overfitting.Formula:If we split the dataset into k subsets (folds), the cross-validation error is computed as:Why is Cross-Validation Important?Reduces overfitting: Ensures the model is not just memorizing data.Improves model selection: Helps compare different algorithms fairly.Efficient use of data: Unlike a simple train-test split, cross-validation utilizes more data for training.Types of Cross-ValidationK-Fold Cross-Validation (Most Common Method)The dataset is split into k subsets (folds).The model is trained on k-1 folds and tested on the remaining fold.This process repeats k times, ensuring each fold is used for testing once.The final performance metric is the average across all k runs.Formula: If X is the dataset, Di is the i-th fold, and M is the model:Example Calculation (K=5)