Overfitting is the case where model performance on the training dataset is improved at the cost of worse performance on data not seen during training, such as a holdout test dataset or new data.
If the performance of the model on the training dataset is significantly better than the performance on the test dataset, then the model may have overfit the training dataset.
I explained the concepts of overfitting and demonstrated how to identify overfitting in Python.
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The Python code is uploaded into [ Ссылка ]
