Feature selection in machine learning | Full course

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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.

Data Science with Marco

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This is extremely helpful and informative. Thanks a LOT!

NulliusInVerba

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

samuelliaw

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

shwetabhat

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

tanyaalexander

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

babakheydari

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!

abhinavreddy

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

mauroSfigueira

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

claumynbega

Sensational video, thank you so much!

michaelmecham

Amazing video and excelent didatic. Congrats for the great quality, helped me a lot!

pedro_tonom

Great explanation. Easy hands-on as well!!

paramvirsaini

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

maythamsaeed

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

mandefrolegesse

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

oluwasegunodunlami

I am currently reading your book and it's amazing

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?

ax

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

chiragsaraogi

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?

alfathterry

I want to make LSTM time series, what should I do for this? I think the situation is different for time series. Would I be wrong if I use what you did? There is both trend and seasonality in the series.

TheSerbes

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

pooraniayswariya

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