Why is overfitting a problem?

What is overfitting?

Overfitting is a common phenomenon you should look out for any time you are training a machine learning model. Overfitting happens when a model learns the pattern as well as the noise of the data on which the model is trained. Specifically, the model picks up on patterns that are specific to the observations in the training data but do not generalize to other observations. And hence the model is able to make great predictions on the data it was trained on but is not able to make good predictions on data it did not see during training....

Why is overfitting a problem?

Overfitting is a problem because machine learning models are generally trained with the intention of making predictions on unseen data. Models which overfit their training data set are not able to make good predictions on new data that they did not see during training, so they are not able to make predictions on unseen data....

How do you check whether your model is overfitting to the training data?

In order to check whether your model is overfitting to the training data, you should make sure to split your dataset into a training dataset that is used to train your model and a test dataset that is not touched at all during model training. This way you will have a dataset available that the model did not see at all during training that you can use to assess whether your model is overfitting....

How to prevent overfitting in random forests of python sklearn?

Hyperparameter tuning is the answer for any such question where we want to boost the performance of a model without any change in the dataset available. But before exploring which hyperparameters can help us let’s understand how the random forest model works....

Importing Libraries

Python libraries simplify data handling and operation-related tasks up to a great extent....

Conclusion

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