Deep Neuro-Fuzzy Systems with Python – With Case Studies and Applications from the Industry : [PDF Download]

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“Underfitting and overfitting happen when you have imbalanced datasets. Suppose for a binary classification problem, you have 90% of the data from one category and the remaining from the other.

In this case, the model will learn most of the things related to the first category and very little related to the second category. This can also happen if you train the model a little too vigorously or add parameters to the model.

Overfitting happens when you train the model a little too long, while underfitting happens when the model is not trained long enough. In other words, you can say that if you train the model to such an extent where error starts to increase, overfitting may happen.

But, if you stop training the model when the error is high and can still be reduced, underfitting may happen.”

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