Overfitting occurs when a model learns the training data too well, capturing both the underlying patterns and noise in the data.

Underfitting happens when a model fails to capture the underlying patterns in the training data and performs poorly.

Overfitting generally occurs when a model is too complex relative to the amount and quality of the training data.

Underfitting often occurs when a model is too simple and cannot capture the complexity of the underlying data.

Overfitting performs excellent on the training data but perform poorly on unseen data

Underfitting results in poor performance on both the training and testing data.

Overfitting can occur when there is noise or outliers in the training data.

Underfitting can occur when the training data is not representative of the true population