Thanks a lot for the detailed discussion on this topic. For the question asked in the video(Which feature to be removed incase of high correlation), I guess among the two, we have to remove the one which least contributes(less correlated) with the target variable. In that way, we will be able to preserve the feature which has high contribution.

If x1 and x2 are strongly correlated then we should check their individual correlation with target and will select the variable which is highly correlated with target and can also check p value for the variables.

Firstly great explanation !! Now coming to your question, we have to check the bi-variate strength between dependent variables with independent variables. The independent variable with weakest strength should choose to remove from model

Amazing pace, crisp word selection and good examples, thank you Aman for great videos !!

awesome video! very clear and beginner friendly, no broken train of thought, very problem-focused

Wow, I think I owe you my mark on the Econometrics final, you blew my mind, I had no idea it was so simple. Thank you!

Simple, Clear and Amazing explanation!!! I think we can remove one of the columns seeing the p value. If p>0.05 then we fail to reject the Null hypothesis for that variable and thus that coefficient value will be equal to 0.Hence that variable will not contribute significantly. Sir pls do make a video on how to use Ridge-Lasso regression to handle multicollinearity.

Never seen someone with such a clear understandable explanation...thank you so much!

The best explaination on whole KZfaq! Thank You.

To omit either X1 or X2, we can use PCA and remove the variable with low variance.

Thank you for detailed explanation. I tried this concept from other channels but was bit difficult to get it. Your way of explaining terms is very simple and which helps to understand subject. Really glad that i visited your channel.👍

I don't know which one should I take. By the way video is great

It is evident that a lot of work goes into developing these very informative videos. Thank you!

Thank you sir... Best explanation

Great explaination sir . Thanks for sharing and making my fundamentals strong

Amazing Explanation Aman, I have a question that VIF and auxiliary regression both use to detect multicollinearity?

If X1, X2 have high correlation, can I choose to drop the X with lower correlation to Y? Based on the correlation matrix

Remove the variable which have low impact on target variable... Sir I hv 2 question 1. If there is multicollinearity in Classification problem. How to handle that 2. What is VIF & how standardization done 3. Can we use standard scaler in regression problem

best explanation....keep the good work up.

If value is less then thresholds value 0.5/0.7 as per the reference suggests. Then we can remove those values

really loved sir what u said i can say that u have great idea of explaining concepts. i can blindly follow u sir

We can create and compare two models based on choosing each of the correlated explanatory variables one at a time and select the model having better R-squared value.

Such a great explanation sir.. Thanks a lot!

Aman, Your videos are great. But there are many videos which have some connection with other, so can you please make a video in which you can say which order to follow the playlists to learn the machine learning from basics. It would be really helpful😅

Very Informative aman

thanks for clear explanation and God bless!

great, easily understandable

is multicollinearity will be problem too in correlations? just focus on getting which variables that correlate, not focus on regression. like in PCA

You are a great teacher! I learnt something new today.

Thank you

You are a genius. Thanks

Good and perfect

Hi Aman, What should we do when the constant term p-value is high? Mostly I see that people keep it without worrying about it. Could you please give an explanation for this?

Hi Aman, hope you are doing well ! I want to ask one thing, what you are mentioning regression models is related to linear models right not the tree based regression models am i correct ? does multicolinearity effects the tree based models ?

Hello sir! Firstly thank you for the video! I have 2 questions if you answer I will be glad: 1) Can we say that we don't need to be concerned about correlated features in for example decision tree-based models? I mean do we need this concept only in linear-based models? 2) Don't we need to touch correlated features when we use Lasso or Ridge regression is that true? Will the model do that by itself in that case? Don't we need to touch?

My question.. 1. Is multicollinearity a concern for predictive modeling. I mean the prediction is altered by neglecting this phenomenon or not. 2. In case of GAM do we have to worry about multicollinearity. 3. How collinearity inflates the variation.

Please please make a vedio how to select drivers in linear regression which drive the sales

Hi Aman, As per my knowledge we can use VIF (Variation Inflation Factor) function, heatmap,Corr() function to remove the multicoliniarity. Please confirm another techniques

Sr ap ko js ne jo answr dia he sb ka answr correct he ap sb ko yes bol rhen hn

Sir can given some ideas on how to know which type of ml models is affected by multicollinearity?

Hi Aman it's very good explanation...please do video on penalised regression like lasso ridge and elastic..too much of mathematics into those please explain in simple way Thank you

Great content.

Thankyou for excellent explanation. I have fews questions please: 1. I used Polynomial features method in sklearn and it significantly improved accuracy of my linear regression prediction model, but i found that the newly created features are correlated with the existing features since i created square and cubes! I understand as per your explanation that it will lead to multicollinearity problem! So i understand that the coefficients are not the true picture, However can i use this type of model for predictions? 2. What would you suggest the threshold correlation value for multicollinearity? Thanks

finished watching

Sorry so it means when there is multicollinearity for example x2 and x3, so if I increase x2, x3 will automatically increased? Great video by the way!

Simple and to the point explaination 🤘

we will drop that feature from the model whose correlation with the dependent variable is lesser as compared to the other one

Thank you!

Great Explanation... At 7.50 you said "that's why we should not have multicollinearity in regression" . So, Is it okay if we have multicollinearity in classification?? Could you please make it clear..

which one will eliminate ? VIF of each features set the threshold >5

excellent explanation

Should we need to remove multicollinearity while building time series model?

09:11 The Data which More Coefficient Value that we have to consider for analysis.

Sir if the there are 3 predictors and one dependent variable. all the three independent variables are highly correlated then which type of regression model can be used. multiple regression can not be used rt?can we use the linear regression? can the tolerance of .1 and the VIF less than 10 not a good enough to indicate that there is no multicollinearity? for your question i think the one with weak correlated one to be removed

I will find the correlation between x1 and y and x2 and y individually and see which one is lesser the one with lesser correlation i will delete it

Hello sir, After reading the comments I saw the answer to your question. They said we have to remove the one which has less correlation coefficient with the target variable due to the correlation matrix. It confused me at one point, Can we say that the coefficients in front of each feature that we get after running the regression model indicate us impact of each feature on the target? So, I mean can I take these coefficients when I decide which feature I have to remove bw two correlated features instead of taking correlation matrix value with the target variable? Can we say that the coefficients in front of each feature actually say the same thing as the value in the correlation matrix with the target variable in this context?

regarding the question- which variable to remove out of a set of highly correlated variables? Can this be answered by PCA (principal component analysis)? or will the PCA weight them the same because they are highly correlated?

Hi...Thanks for the nice explaination. Have a question that is multicollinearity a problem for linear regression only? if not then how its a problem for non-linear regression?

Hi Aman, nice work, keep it up.....i have a doubt that why normal distribution is so important? why we need our independent variable should show normal distribution for a good model? i am not finding a satisfying answer. can you please help?

Great.

Is multicollinearity is the problem for neural network?

Awesome explanation, I have a question: if I have nominal,ordinal and continuous variables how can I find multicollinearity among them?

You always rocks :)

The independent variable with higher correlation among the similar factors should be removed

great explanation

this shit is pure gold

Thnks sr g I think uncecessary variable remove

can we remove highly negatively correlated features also or not? someone reply, please

Remove that feature which is in less correlation with target.

Hi Aman, Could you please make a detailed video explaining the difference between Gradient Boost, AdaBoost and ExtremeGradientBoosting? Why is AdaBoost called adaptive? Is it only because it edits the weights of the misclassified instances? XGBoost and GradientBoost also are adaptive in that way, arent they? Also, why are XGBoost and Gboost more robust to outliers than AdaBoost despite all of them having a term of log in their loss functions? Would really appreciate your reply. Thanks

Great video. Please create video on python implementation of Lasso and ridge regression

Multicolinearity is problem in classification as well right .@3:57

I think which variable highly CO relate with target variable

Sir what to do if the multi collinearity is affecting the binary classification problem

Should I check multicolinearty for classification problem?

at least two variables!

It depends on feature importance. the feature with less importance will be dropped. correct me if am wrong :0

I want to be a data analyst but I want sequential courses from you please guide

Hi Aman, I have 9 categorical and 6 numerical columns and it's a regression problem. So I can find the correlation between numerical using correlation heatmap but how to find the relation between categorical..?? Can I use chi square test..?? If I use I am getting all 9 categorical are dependent on each other. So what should be my next step..?? Please guide me. Thanks

Sir please explain Lasso and ridge if you made it,link pl.

Sir plz do the video for post pruning decision tree

Is Machine learning better than deep learning or deep learning better than machine learning

Jo less Cor related ho usko remove karna hai

Neither x1 or x2