Feature selection in machine learning | Full course

Full source code on GitHub: github.com/marcopeix/youtube_...
Introduction - 0:00
Initial code setup - 2:19
Variance threshold - 11:04
Variance threshold (code) - 13:02
Filter method - 19:39
Filter method (code) - 21:27
RFE - 29:08
RFE (code) - 30:42
Boruta - 37:12
Boruta (code) - 41:21
Thank you - 46:35
A full course on feature selection in machine learning projects.
We first cover a naive method based on variance. Then we move on to filter method and wrapper method like recursive feature elimination or RFE. Finally, implement the Boruta algorithm.

[@ax5344]

I like the logic of this video. You showed the baseline, then three additional methods, then compare them in the end. Thanks a lot for sharing the technique. The feature/target matrix is also very helpful.
My question is the principle or concept behind the filter method, RFE, and boruta. Is it possible to do a video on them?

[@lecturesfromleeds614]

Marco's the man!

[@mauroSfigueira]

Hugely informative and educational content. Many feature engineering videos are not that instructive.

[@tanyaalexander1460]

I am a noob to data science and feature selection. Yours is the most succinct and clear lesson I have found... Thank you!

[@samuelliaw951]

Really great content! Learnt a lot. Thanks for your hard work!

[@abhinavreddy6451]

Please do more Data science-related content, It was very helpful I searched everywhere for feature selection videos and finally landed on this video and this was all I needed, the content is awesome and the explanation is as well!

[@shwetabhat9981]

Woah , much awaited 🎉 . Thanks for all the efforts put in sir . Looking forward to more such amazing content 🙂

[@babakheydari9689]

It was great! Thanks for sharing your knowledge. Hope to see more of you.

[@claumynbega1670]

Thanks for this valuable work. Helps me learning the subject.

[@michaelmecham6145]

Sensational video, thank you so much!

[@Loicmartins]

Thank you very much for your work!

[@maythamsaeed533]

very helpful video and easy way to explain the content. thanks alot

[@paramvirsaini2806]

Great explanation. Easy hands-on as well!!

[@datasciencewithmarco]

Thank you!

[@mandefrolegesse5748]

Very interesting explanation and clear to understand. I was looking for this kind of tutorial. Subscribed👍

[@tongji1984]

Dear Marco Thank you.😀

[@oluwasegunodunlami7360]

Wow, this video is really helpful, a lot of interesting methods were shown. Thanks a lot.
I like to ask you to make a future video covering how you perform feature engineering and model fine tuning 1:49

[@roba_trend]

interesting content much love it🥰

[@scott6571]

Thank you! It's helpful!

[@datasciencewithmarco]

Glad it helped!

I am currently reading your book and it's amazing

[@jmagames2766]

what is the name of the book plz

[@imadsaddik]

Thank you for sharing

[@dorukucar7105]

pretty helpful!

[@eladiomendez8226]

Awesome video

[@datasciencewithmarco]

Thanks!

[@alfathterry7215]

in Variance threshold technique, if we use Standard scaler instead of Minmax scaler, the variance would be the same for all variables.... does it means we can eliminate this step and just use standars scaler?

[@pooraniayswariya997]

Can you teach how to do MRMR feature selection in ML?

[@chiragsaraogi363]

This is an incredibly helpful video. One thing I noticed is that all features are numerical. How do we approach feature selection with a mix of numerical and categorical features? Also, when we have categorical features, do we first convert them to numerical features or first do feature selection. A video on this would be really helpful. Thank you

[@haleematajoke4794]

You will need to convert the categorical features into numerical format by using label encoding which automatically converts it to numerical values or custom mapping where u can manually assign ur preferred values to the features. I hope it helps

[@haleematajoke4794]

You will have to do the conversion before feature selection because machine learning models only learn from numerical data

[@therevolution8611]

can you explain how we are performing feature selection for the multilabel problem?

[@user-cl3ej5mi7k]

You can convert the label to numerical features by replacing them with numbers. If you have 3 labels in a feature, you could represent them with 0,1,2 there are different methods to use. Simpler one is .replace({})

[@nabeel_kaleel]

subscribed

[@cagataydemirbas7259]

Hi, when I use randomforest , DecisionTree and xgboost on RFE , even if all of them tree based models, they returned completely different orders. On my dataset has 13 columns, on xgboost one of feature importance rank is 1, same feature rank on Decisiontree is 10, an same feautre on Randomforest is 7. How can I trust wich feature is better than others in general purpose ? İf a feature is better predictive than others, shouldnt it be de same rank all tree based models ? I am so confused about this. Also its same on SquentialFeatureSelection

[@datasciencewithmarco]

That's normal! Even though they are tree-based, they are not the same algorithm, so ranking will change. To decide on which is the best feature set, you simply have to predict on a test set and measure the performance to make a decision.

[@mrthwibble]

Excellent video, however I'm preoccupied trying to figure out if having wine as a gas would make dinner parties better or worse. 🤔

[@roba_trend]

i tried to search under your github aint get the data where is the data you work on?

[@datasciencewithmarco]

The dataset comes from the scikit-learn library! We are not reading a CSV file. As long as you have scikit-learn installed, you can get the same dataset! That's what we do in cell 3 of the notebook and it's also on GitHub!

[@nikhildoye9671]

I thought feature selection is done before model training. Am I wrong?

[@keerthana7353]

Yes correct