A scenario where a Machine Learning model performs well on the training data but poorly on unseen test data. Overfitting indicates that the model has learned noise and details specific to the training set rather than general patterns. Learn more.
A scenario where a Machine Learning model performs well on the training data but poorly on unseen test data. Overfitting indicates that the model has learned noise and details specific to the training set rather than general patterns. Learn more.
