Underfitting and overfitting are issues that can occur in supervised machine learning problems. These problems involve training a model to learn a mapping from input data to output labels based on a given dataset.

Overfitting

The model shows high accuracy during the training phase but fails to show similar accuracy during the testing phase.

The model is too complex and learns not only the underlying patterns but also noise and outliers specific to the training set. While it performs exceptionally well on the training set, it struggles to generalize to new, unseen data in the testing phase.

Underfitting

The model fails to show satisfactory accuracy during the training phase.

The model is too simple and fails to capture the complexities in the data, resulting in poor performance in both the training and testing phases.

Actual Good fitting:

Training and testing overall performance is good.

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References:

https://www.superannotate.com/blog/overfitting-and-underfitting-in-machine-learning
https://www.geeksforgeeks.org/underfitting-and-overfitting-in-machine-learning/