What is over-fitting and under-fitting? How do you deal with it?

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Over-fitting is when a model is too complex and fits the training data too well, while under-fitting is when a model is too simple and cannot capture the underlying patterns in the data.

Over-fitting occurs when a model is trained too much on the training data and starts to memorize it instead of learning the underlying patterns.
Under-fitting occurs when a model is too simple and cannot capture the complexity of the data.
To deal with over-fitting, one can use techniques such as regularization, early stopping, or reducing the complexity of the model.
To deal with under-fitting, one can use techniques such as increasing the complexity of the model, adding more features, or increasing the amount of training data.
Examples of over-fitting include a decision tree with too many branches or a neural network with too many hidden layers.
Examples of under-fitting include a linear regression model for a non-linear problem or a decision tree with too few branches.

What is over-fitting and under-fitting? How do you deal with it?

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