Step Forward, Step Backward and Exhaustive Feature Selection of Wrapper Method

In this video, we will learn about Step Forward, Step Backward, and Exhaustive Feature Selection by using Wrapper Method. The wrapper method uses combinations of the variable to determine predictive power, to find the best combination of variables, computationally expensive than the filter method, To perform better than the filter method, and not recommended on high number of features. The wrapper methods are of three types Subset selection(Exhaustive Feature Selection), Forward step selection and Backward step selection(Recursive Feature Selection)

🔊 Watch till last for a detailed description
03:02 What is the Wrapper method?
08:08 Use of mlxtend in wrapper method
16:51 Step forward feature selection
28:40 Step backward selection
32:15 Exhaustive feature selection

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[wasgeht]

One of the beste Feature Selection playlist on YouTube maybe the best

[cssuresh]

Hi
This is one of the need and clean explanation on wrapper methods. Do you have any online courses on dimensionality reduction and feature engineering ?
Thanks

[AkshayKumar-xosk]

Where can we find the notebook used in this video? The link given in description doesn't work. Can let us know how can I find the notebook used in this video?

[jfowler]

Hi, I resolved the mixtend problem. What is the optimum feature size for the Wrapper Methods? If the number of features for my data exceeds this optimum size, then would a filter method be my only choice for reducing the feature size without an extremely large processing time? If so, would this mean that my accuracy would not be as good with these filtering methods?

[firasalshatnawi]

This is so helpful, thank you very much!

[rajbir_singh]

You have taken are features are numeric in nature. What us we have category feature. Do we need to do need to create dummy variable and then perform this task?

[kritiohri]

Sir in backward feature selection, in the diagram that was shown why, was feature 3, 4 5 wasn't considered in the last column
?

[amanchaudhry]

The variables selected from any of these methods does not include any intercept. So does this mean we do not require the intercept or we need to add it to our actual model.

[gabrielfreire]

is there a way we could access the notebook?

[soumyadrip]

CAN I USE EFS(Exhsaustive Feature Selection) IN CATEGORICAL VARIABLES TOO?
SIR, PLEASE ANSWER THE QUESTIONS....

[anuragsingh-ptcy]

ANOVA, Pearson correlation, and variance thresholding... any video on this filter method techniques??

[ggdorleon]

I have a question:
How does wrapper select the first features, in which order ? Is this something that it does randomly or not ? I understand the methods builds a model from several features subset and keeps the best one, but how does it selects the first feature to add to the first created subset ? Thanks

[mooncake]

How do you determine what number you are supposed to give to k_features ? Is there a automatic way to do this where it selects the best possible score ?

[adithyaramula]

How to deal with categorical features in the data set? should we encode them?

[aggarwalr]

How can we apply greedy approaches such as hill climbing or best first search method?

[gaayathri]

can anyone explain what is k feature why we are selection k feature as random manner
..

[shreyanshgupta]

i am unable to find the repository in the GitHub link .could you please attach all your files?

[ASPIRANT_IN_ACTION]

Could you please share this notebook? The github link is not working.

[aravindravikumar]

For regression problem, do we need to use RandomForestRegressor instead of RFclassifier in SFS ?

[sushreedeepajena]

Getting syntax error- invalid syntax 'verbose =2'