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can you please explain what is overfitting? How can it be overcome in Machine Learning?

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When the machine learning algorithm tries to capture all the data points, and hence, as a result, captures noise also, then overfitting occurs in the model. Due to this overfitting issue, the algorithm shows the low bias, but the high variance in the output. Overfitting is one of the main issues in machine learning.

Methods to avoid Overfitting in ML:

1) Cross-Validation

2) Training With more data

3) Regularization

4) Ensembling

5) Removing Unnecessary Features

6) Early Stopping the training.
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