Model Fitting: Overfitting, Underfitting, and Balanced

Understanding model fitting is important for understanding the models’ poor accuracy.

Overfitting: When the model performs too well on training data then it reduces the model flexibility for new data.

Underfitting: When the model performs poorly on the training data. It’s often caused by an excessively simple model.

Both overfitting and underfitting lead to poor performance in real time.

Balanced: Bbalanced models would show better accuracy on new data.

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