Resampling and Regularization | Data Science with Marco

Get the notebook and the dataset: github.com/marcopeix/datascie...
📚 Theory: 0:00 - 5:17
🐍 Code: 5:18 - 14:40
In this video, we cover resampling and regularization in Python. We cover 3 different approaches to resampling: validation set, leave-one-out cross-validation (LOOCV) and k-fold cross-validation.
Then, we introduce two resampling methods: ridge regression and lasso.
Finally, we apply all those methods in Python and see how they can improve our models.
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[@bkpusprajkumar8744]

Can you explain the resampling and regularization with Classification model.

[@rionazhang3718]

Hello, why do you not do train_test_split here but did in portfolio project?

[@datasciencewithmarco]

Hi! The coding portion was really to focus on regularization. After this video, (decision trees and onwards) I split the data into a train/test set.

[@cs013]

As a Data Scientist is there any need to perform resampling? Explain it.

[@datasciencewithmarco]

Yes! Especially when it comes to cross-validation, it's a way to make sure that models are robust to make predictions on unseen data

[@sauravroy3420]

I am a follower of you. How can I contact you ?

[@seleldjdfmn221]

Amazing vid. How often should Someone upload? I Really Want to be youtube friends :D