Very relatable when u said most ml developers are .fit and .predict people lol. I started learning ml a month ago and same way thought ml is easier than I thought but when I started learning more complicated algos I'm learning the importance of stuff like this, be it feature engineering or selection or another thing I got to learned today: class imbalance. I'm learning off of internet and projects so it's a little hard road but youtubers like u are blessings 🙌 . An idea- Can u maybe make a vid explaining a real life ml problem and how would u go about it, like churn prediction, sales prediction, etc
may my God bless you sir for this distinctly and succinctly explained lecture on RFE and RFECV. It was revealing and understandable for an average mind. Pls open a school sir, i'd gladly be your student.
No one touches these topics the way you're doing. It makes concept more clear. Please can you also make a video on parameters we pass in each algorithm. E.g. init,n_init,n_clusters,max_iter in kmeans clustering. It confuses a lot. Due to this many people don't touch those parameters
Thanks. Keep going. 👍🏼 I wanted to know exactly, based on what concept or what parameters it will eliminate?! Hope I will get the answer if I learn little more. Another clarification required: RFE inside algorithms working varies from each other but output features must be same right?! If that varies which algorithm to trust?! Good explanation.
Thankyou for an informative lecture. As you mentioned when we apply RFE using Decision Tree algorithm we get different features importances than when we apply RFE using Linear algorithm (coefficients in this case). My question is how do we interpret it i.e. how do we conclude which feature is the most important one in such a case?
I really appreciate the explanation though, I cannot agree with your point about the feature importance in linear regression is measured by their coefficients. I think we should use p-value to measure the importance instead.
Thanks for explaining .. I am getting these two error : 1. __init__() takes 2 positional arguments but 3 were given 2. 'RFE' object has no attribute 'ranking_'
Hey Aman, As you said, you will upload videos about RFE and RFECV with python examples. I looked everywhere in your channel but couldn’t find those. Could you please give me the link to those videos?
I have query. U put decision tree classifier or logistic regression for rfe to select features. After using rfe do we have to apply classification algorithm to generate model using different algorithm. Can we use svm, rf, knn algorithm.
Thank you for the RFE video. Your explanation was very good! I have a question: I have an already existing linear equation with 7 variables (feature) including the target feature. However, I want to elimination 3 less important features. Please, how can I custom fit this equation (algorithm): that is I want a user defined algorithm for the RFE. This will help me to eliminate the less important features.
Great explanation, thanks! but i have a question... after using this RFE, we got useful and useless features. what are the parameters used to get those features? cv scores? or something else?
Let's suppose you create an instance with the name "rfe". Use rfe.support_ to get the useful and eliminated features. It returns a Boolean array where True means that feature is kept and False means that feature is eliminated
Many Thanks for your excellent style to teach important basics of data science. Would you please give the link of your video showing RFE/RFECV usage in Python; I am looking for that video particularly. Thanks in advance.
Thanks Khalid. I think I am unable to find it now. Probably I will search once more or meanwhile this link will help machinelearningmastery.com/rfe-feature-selection-in-python/
Would you please clarify one thing; I am supposed to use RF algorithm for my prediction problem. Do I still need to screen the predictor variables by RFE/RFECV before training the model?
First off, thank you for a great explanation. I do have a question though. When does the RFECV algorithm know when to stop removing features? With RFECV, does the algorithm stop removing features once the CV score goes down ? Thanks.
Amazing content Aman sir!! cheers.. I have a doubt, on what parameter the RFE is eliminating the features? Is it the r2 score or the adjusted r2 score? How is it different from forward and backward elimination?
Kindly do not confuse RFE with search algorithms that search over all possible subsets of features. RFE does something similar, but not check all possible combinations.
Thanks Aman for this very informative video! Can you please tell on what basis feature importance is decided by RFE algorithm? is it based on p-value and VIF?
My understanding is that it is based on the algorithm that you input into it. So in the case of this example, a Decision Tree algorithm will use the feature importance which is (using the TowardsDataScience definition) "the decrease in node impurity weighted by the probability of reaching that node". RFE is like a secondary step to add on to it. Like backward stepwise regression, which isn't a standalone thing, you still need to decide which statistic you are basing your steps off of. I could be off though and someone can correct me if I am wrong!
Sir, In wrapper exhaustive feature selection method we find the optimal subset of feature using certain Machine learning algorithm, so in finding optimal subset can we use any algorithm or there are only specific algorithms that should be used only? I’m confused in implementing this
RFE eliminates by taking all the features in the model , ranking them based on coefficients then removing the features whereas in the case of BFE , for eg one feature is dropped then possible combinations of features to model then it checks the accuracy of model , then next iteration based on selected features... Hope it explains
Hi Aman, Can you please make a video on propensity model.Have been looking for it for a long time but couldn't get good resources online. Please help me in understanding it using python
Yes Malavika, these models are mostly used in Risk Domain in Finance or in marketing analytics type of scenario. I have some friends working on these models, I will get an idea and try to present.
According to this, we do REF on the training data. That means we do REF after splitting the initial data set into TRAIN and TEST samples. if so, what happens to the recursive features in the TEST set?
I watched your channel for the first time. I must say you are a great lecturer. Your explanations are superb! Please keep uploading more videos
Thanks Ravindu.
Very relatable when u said most ml developers are .fit and .predict people lol. I started learning ml a month ago and same way thought ml is easier than I thought but when I started learning more complicated algos I'm learning the importance of stuff like this, be it feature engineering or selection or another thing I got to learned today: class imbalance. I'm learning off of internet and projects so it's a little hard road but youtubers like u are blessings 🙌 .
An idea- Can u maybe make a vid explaining a real life ml problem and how would u go about it, like churn prediction, sales prediction, etc
I create videos on these topics . you can watch here:
th-cam.com/video/SSk5T5X5V40/w-d-xo.html
Omg i really love ur explanation 😭👍
Cheers.
Fantastic and thorough explanation. Thank you, Aman, for helping out the community with these videos!
Just discovered your channel, and your explanations are amazing! Will check out more videos. Thanks for taking the time out to make such content:)
Thanks Hari. Your words mean a lot
Aman you are rocking in your every video..... inspiring me at the age of 40 😁 to go for it
Cheers Sir 😀
Whenever coming on your YT Page, just getting Good information! Well Done Aman!!!:)
Thanks for your words Himalay.
Aman sir ..very well explained & Rfe concept is understood very well..i have been enrolled for carrieer in datascience.
may my God bless you sir for this distinctly and succinctly explained lecture on RFE and RFECV. It was revealing and understandable for an average mind. Pls open a school sir, i'd gladly be your student.
You are most welcome
Supper explanation as usual, thank you
As usual great explanation Aman...
Thanks a ton Sandeep.
Exellent video, for me you explained it with exactly the right amount of depth!
Thankyou so much for this topic 🙏
Keep watching Pritam.
Thank you, Aman, for explaining this so well! I just subscribed to your channel.
Good one again.
Thank you.
Hi, Thanks for the awesome explanation. Could you also talk about Genetic algorithm?
Thanks, for sure.
Hi sir, this is nice, thanks for your super explanation
Thanks Nived.
finished watching
Aman tell me one thing on when to use which technique of feature selection
No one touches these topics the way you're doing.
It makes concept more clear.
Please can you also make a video on parameters we pass in each algorithm.
E.g. init,n_init,n_clusters,max_iter in kmeans clustering.
It confuses a lot. Due to this many people don't touch those parameters
Thanks Pramod. Noted.
Thanks for this explanation .. keep continuing good work sir ..... I learnt many topics which has helped me in most cases
Keep watching Charan. Thanks a lot.
Hi Aman,on what basis RFE choose best features
Nice explanation Aman .. ❤
Thanks a lot.
Thanks Aman
Thank You very much sir!
Most welcome!
becoz i am a new learner...can u make a video for EDA part to make it easy to go through..
Where is the next video for this? This video is really helpful. Can you please provide link for next video of the same.
Hi Mayank, thanks for asking, please search on playlist.
Can you also explain Orthogonal Matching Pursuit?
Is it a must to split data into x_train, x_test, y_train, y_test when using RFE?
Kindly upload video of REF with example
Is there any way to use multiple models in rfe and then select the features from the model which give the best performance?
Yes possible, you can run in loop all models.
Good video, thank you. Can you please share link of follow up video?
Thanks. Keep going. 👍🏼
I wanted to know exactly, based on what concept or what parameters it will eliminate?! Hope I will get the answer if I learn little more.
Another clarification required: RFE inside algorithms working varies from each other but output features must be same right?! If that varies which algorithm to trust?!
Good explanation.
Great, Thanks a ton!
You're welcome!
Very useful information. Thank you :)
Thank u for the super information. Which is the best method to select the features
You are welcome Bharath, Video is on the way, Sunday 4PM IST
Thankyou for an informative lecture. As you mentioned when we apply RFE using Decision Tree algorithm we get different features importances than when we apply RFE using Linear algorithm (coefficients in this case). My question is how do we interpret it i.e. how do we conclude which feature is the most important one in such a case?
Great video!
Thanks Ahmed.
Can you suggest that Bluetooth transfer can be seen through data leakage software. If the system is offline
I really appreciate the explanation though, I cannot agree with your point about the feature importance in linear regression is measured by their coefficients. I think we should use p-value to measure the importance instead.
You have a valid point, can we say its a combination of both through which I can meansure how important my variable is?
How the DT model will select which is least feature? Can you answer please?
Thanks for explaining ..
I am getting these two error :
1. __init__() takes 2 positional arguments but 3 were given
2. 'RFE' object has no attribute 'ranking_'
Check latest package and function names. easy issue to fix.
thank u sir
Welcome Krishna
Hey Aman,
As you said, you will upload videos about RFE and RFECV with python examples. I looked everywhere in your channel but couldn’t find those. Could you please give me the link to those videos?
I have query. U put decision tree classifier or logistic regression for rfe to select features. After using rfe do we have to apply classification algorithm to generate model using different algorithm. Can we use svm, rf, knn algorithm.
Yes don't confuse feature selection with modelling.
RFE - Feature selection technique
svm, rf, knn algorithm.
Thank you for the RFE video. Your explanation was very good!
I have a question:
I have an already existing linear equation with 7 variables (feature) including the target feature. However, I want to elimination 3 less important features. Please, how can I custom fit this equation (algorithm): that is I want a user defined algorithm for the RFE. This will help me to eliminate the less important features.
Great explanation, thanks!
but i have a question... after using this RFE, we got useful and useless features.
what are the parameters used to get those features? cv scores? or something else?
Let's suppose you create an instance with the name "rfe". Use rfe.support_ to get the useful and eliminated features. It returns a Boolean array where True means that feature is kept and False means that feature is eliminated
sir, can you explain the difference between selectkbest and RFE..
Sure, will add this topic in my to do list.
Which one is better forword feature selection or recursive feature elimination.
As it depends on case to case.
what basis feature importance is decided by RFE algorithm?
Nicely explained !!
Can you make some videos on How to do Outliers detection with coding implementation as well ??
Ok Saswat. Sure
Many Thanks for your excellent style to teach important basics of data science. Would you please give the link of your video showing RFE/RFECV usage in Python; I am looking for that video particularly. Thanks in advance.
Thanks Khalid. I think I am unable to find it now. Probably I will search once more or meanwhile this link will help
machinelearningmastery.com/rfe-feature-selection-in-python/
@@UnfoldDataScience Many thanks for the link; It's helpful. I will also be waiting for your VIDEO.
Would you please clarify one thing; I am supposed to use RF algorithm for my prediction problem. Do I still need to screen the predictor variables by RFE/RFECV before training the model?
First off, thank you for a great explanation. I do have a question though. When does the RFECV algorithm know when to stop removing features? With RFECV, does the algorithm stop removing features once the CV score goes down ? Thanks.
Good question!, we can set this throguh parameters
HI, based on which criteria does the model decides the importance of the feature?
Thank you for amazing video! We need some examples in python! Please could you make some practic examples?
Thanks Aidar. Sure.
Amazing content Aman sir!! cheers..
I have a doubt, on what parameter the RFE is eliminating the features?
Is it the r2 score or the adjusted r2 score?
How is it different from forward and backward elimination?
Kindly do not confuse RFE with search algorithms that search over all possible subsets of features. RFE does something similar, but not check all possible combinations.
Thanks Aman for this very informative video!
Can you please tell on what basis feature importance is decided by RFE algorithm? is it based on p-value and VIF?
My understanding is that it is based on the algorithm that you input into it. So in the case of this example, a Decision Tree algorithm will use the feature importance which is (using the TowardsDataScience definition) "the decrease in node impurity weighted by the probability of reaching that node". RFE is like a secondary step to add on to it. Like backward stepwise regression, which isn't a standalone thing, you still need to decide which statistic you are basing your steps off of. I could be off though and someone can correct me if I am wrong!
thanks buddy
Welcome
Sir, In wrapper exhaustive feature selection method we find the optimal subset of feature using certain Machine learning algorithm, so in finding optimal subset can we use any algorithm or there are only specific algorithms that should be used only? I’m confused in implementing this
Can you make a video on LDA its derivation of the discriminant function
Noted Kunal. Thank you.
Can you please post the link where python program is also explained on the same topic.
drive.google.com/drive/folders/1XdPbyAc9iWml0fPPNX91Yq3BRwkZAG2M
Very Nice!
Thank you Umair! Cheers!
So how RFE is treated differently than backward elimination techniques
RFE eliminates by taking all the features in the model , ranking them based on coefficients then removing the features whereas in the case of BFE , for eg one feature is dropped then possible combinations of features to model then it checks the accuracy of model , then next iteration based on selected features... Hope it explains
why rfecv don't work for neural network and svm rbf kernel?
Not work meaning?
Sir it is also known as backward elimination feature selection? Or it is different
Similar to that little different
Nice... Can you pls tell how to REF METHOD write in python..
Sure Kunal.
SIR PLZ UPLOAD PYTHON IMPLEMENTATION...YOUR NEXT VIDEO
Will upload soon, its simple only
Hi Aman, Can you please make a video on propensity model.Have been looking for it for a long time but couldn't get good resources online. Please help me in understanding it using python
Yes Malavika, these models are mostly used in Risk Domain in
Finance or in marketing analytics type of scenario. I have some friends working on these models, I will get an idea and try to present.
@@UnfoldDataScience thank you so much
Then what is the diff between this and backward elimination?
RFE - target individual variable, backward elemination - target model as a whole
Its doesn't seem to be RFE you're explaining backward elimination method
Will check.
No it is rfe...backwards elimination is totally different
According to this, we do REF on the training data. That means we do REF after splitting the initial data set into TRAIN and TEST samples. if so, what happens to the recursive features in the TEST set?
You score model only on the features which were used to train the model.
kya new batya
finished watching
Great video!