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Preventing overfitting: Regularization

The ultimate goal of any Machine Learning model is making reliable predictions on new, unknown data. Hence, while training our algorithm, we always have to keep in mind that having a good score in our train set doesn’t necessarily mean our model will adapt to new data well. Indeed, whenever we have a model which perfectly fit our training data, we are probably incurring in overfitting: our model has too many parameters which cannot be justified by data, hence it is way too complex and heavy.




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Valentina Alto

Valentina Alto

Cloud Specialist at @Microsoft | MSc in Data Science | Machine Learning, Statistics and Running enthusiast

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