Machine Learning Fundamentals › Overfitting & Underfitting

What are common mistakes to avoid with Overfitting & Underfitting

Intermediate

Answer

A common mistake is making a model more and more complex to chase higher training accuracy, which usually makes overfitting worse instead of helping the real performance.

Real-world example A student who memorizes exact exam questions without understanding the topic is like an overfit model, while a student who barely studies at all is like an underfit model.

Common follow-ups: What is regularization, What is the bias variance tradeoff, What is cross validation

Machine Learning Fundamentals Deep Learning Supervised & Unsupervised Learning