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?
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.
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
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
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.
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
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
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?
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!
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!
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({})
