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What are common mistakes to avoid with Cross-Validation Techniques

Intermediate

Answer

A common mistake is applying cross validation after already tuning the model on the full dataset, which can still leak information and give overly confident results.
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