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Machine Learning Fundamentals › Cross-Validation Techniques

Why is Cross-Validation Techniques important in Machine Learning Fundamentals

Beginner

Answer

It gives a more reliable estimate of model performance than a single train test split, because it reduces the chance that results depend on one lucky or unlucky split of the data.
Real-world example A team building a disease prediction model uses five fold cross validation to make sure their reported accuracy is stable and not just the result of one favorable data split.

Common follow-ups: What is a validation set, What is overfitting, What is model evaluation

Machine Learning Fundamentals Deep Learning Supervised & Unsupervised Learning
All 7 Cross-Validation Techniques questions