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Commonly confused · AI & Machine Learning

Overfitting vs Underfitting: What's the Difference?

Overfitting happens when a model learns the training data too closely, including noise, so it performs well on training data but poorly on new data. Underfitting happens when a model is too simple to capture the underlying pattern, so it performs poorly on both.

Overfitting

Full definition

vs

Underfitting

Full definition

	Overfitting	Underfitting
Training performance	Very good	Poor
Validation/test performance	Poor	Poor
Bias / variance	Low bias, high variance	High bias, low variance
Typical cause	Too complex model, too little data, too many epochs	Too simple model, too few features, too much regularization
Fixes	More data, regularization, dropout, early stopping, simpler model	More features, more complex model, train longer, less regularization

Remember it

Overfitting memorizes the textbook; underfitting never opened it.

Exam tip

A large gap between training and validation accuracy signals overfitting. Exams pair overfitting with high variance and underfitting with high bias.

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AWS Certified Machine Learning Engineer - Associate Google Cloud Professional Machine Learning Engineer Databricks Certified Machine Learning Associate Microsoft Azure Data Scientist Associate AWS Certified AI Practitioner CompTIA DataAI

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