Overfitting & Underfitting

Summary

Overfitting means a model memorized training data noise and fails on new data; underfitting means it's too simple to capture real patterns even in training data — the two failure modes every model sits between.

How to Learn This

1Plot training vs validation error as model complexity increases, and identify both zones.
2Learn the common symptoms: overfitting shows a big train/test performance gap.
3Practice deliberately overfitting a tiny model, then simplifying it back down.

Train / Test / Validation Split

Bias-Variance Tradeoff

More topics in Machine Learning Fundamentals

Supervised vs Unsupervised vs Reinforcement Learning Train / Test / Validation Split Bias-Variance Tradeoff Cross-Validation Linear Regression Logistic Regression Decision Trees Random Forests Gradient Boosting (XGBoost, LightGBM)K-Nearest Neighbors Support Vector Machines Naive Bayes

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