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Underfitting And Overfitting In Machine Learning Models

This article explains the underfitting and overfitting in Machine Learning models.

What Is Underfitting?

Underfitting happens when the model is too simple to capture the underlying pattern in the data.

Increase Model Complexity, Such As Using Deeper Neural Networks Or Increasing The Number Of Features.
Train longer, such as increasing epochs in training.

Overfitting means the model performs very well on training data but poorly on test (unseen) data.

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