Thanks, Krish. One way that you suggested is to Drop the feature. There is one more thing that we can do is combine the features which we also called them as an interaction term (like multiply Age and years of experience, and then run your OLS model). This also seems to control collinearity to some extent
Hello Krish, Thanks for the amazing explanation. I have one question as - Is it necessary to check summary and then conclude about multicollinearity. Can we proceed only with corr() function and conclude about multicollinearity. Thanks in advance, :) #multicollinearity #Krish Naik #Regression
Krish, could you explain why AGE has the high P value and not Years of Experience? If you remove Years of Experience from the table, then Age has a P value of 0, as this makes sense as in the data as Age increases, Salary increases accordingly.
Unless you calculate Variance Inflation Factor, you cannot be certain that multicollinearity is nor present. Ideally you should be calculating partial correlation coefficient and compute (1/1-partial correlation coefficient) and see if this is greater than 2
Nice presentation! Dropping a feature is the one solution, sometimes you can’t drop and you may need that feature in the analysis what would you do? Combine features or division or multiplication? Or use factor analysis? Mostly the cases like Marketing Mix modeling the similar issues arise often. Please Try videos like Marketing Mix modeling, Optimization, and forecasting estimating ROI, IROI, response curves....
I think, instead of removing any feature we can combine both correlated features (in this context you talked about) Age+YearsOfExperience=> Seniority level. Problem solved. (domain knowledge required for feature engineering).
Hi Krish, Nice explaination of the concept. If you could make a video on Assumptions of Linear Regression ( theory and practical) , that would be of great help for us.
Hey Krish, do we really need to look at Std. error? I think looking at p-vaue is enough as p-value is calculated based on t-statistic, which is calculated based on coeff. and std. error. Correct me if I am wrong, thanks
1.Linearity 2.Homoscedascity 3.Multivariate normality 4.Indepence of errors. 5.Lack of multicollinearity. Please do a video on it. I am not getting these concepts.
Wait if the newspaper column/feature is not correlated then how do we come to the conclusion that it can be dropped as the P-value is high? Are we making the conclusions based on the T-test values here as well?
So that means we should now have 0 expenditure on newspaper? And after removing the newspaper feature, if we do OLS again, can there be any other feature with p-value less than 0.5 in the new OLS model?
An imp ques, if anyone knows, please answer ---> "Why we give OLS, the X value(which is constant) and not the x values(which are our independent features)? if we are not giving the independent values to OLS, then how can it is showing the table in output(with X and y?)"
So to sum up, to identify multicollinearity, we use OLS (ordinary least squares) method to check the model summary for std error (values should be less not high), Rsquare value (should tend towards 1), p value (should
Hi Sir, can we directly find covariance matrix of the feature data and find the determinant of the covariance matrix if it is Zero then we can directly say that our covariance matrix is singular and if it is singular then it having dependent variable. so there is Multi-Collinearity in our data ...
at the last, you said 1 unit decrease in newspaper exp will result in one unit increase in sales. can you plzz explain it. because according to me it will result in change in profit but not in sales
for ex if here is how you have done made the regressor: reg=LinearRegression() reg.fit(x_train,y_train) then you can predict it like: reg.predict([[100]]) and you will get the predicted value if you have trained for multiple features, then use like reg.predict([[100,200]]) according to the dataframe you have trained!
I was looking for Multicollinearity problem in interaction terms. How to solve this problem? One suggestion is centring the variable! But still confused! When one of my interaction variables is dichotomous and coding =0 is important for me, then if I use centering, 0 coding will not be there anymore! So how to solve this? Can you please clarify this for me? Thanks
If I remove years of exp instead of age just by correlation matrix. It will work right? I don't have to check for p values as if I can construct a LR model with just age could change the p value? Any thoughts @channel
Hi krish kindly please answer to this question of mine for under and oversampling data the techniques u have mentioned is applied on numneric datatypes for categorical datatypes first we have to encode in some 0,1 then further SMOTETomek and neamiss wil apply that is the proper way to deal ?? and if we use trees approach then it will straight away deal this imbalance and encoding thing itself ?? Please krish reply to this question it will be a big help!
@@krishnaik06 sir thanks a lot i m just a beginner follow ur each video thoroughly . Sir can u make a video on how to built ur resume for guys looking for transition in ds.
Hi Karish, i have one question, which i am facing daily basic in realtime, How to detect heteroscedasticity and what is the method to rectify it in python ?
There are many tests available. You can research on Bartlett's test. It assumes that the samples come from populations having same variances. There is another called Goldfeld quandt test. To rectify in python, what I Believe is that when working with MLR, if the normality assumption is satisfied then, you shouldnt get heteroscedasticity. So if we have variables that aren't following normal distribution, they can be transformed (refer Krish's video for that too) and then I dont think heteroscedasticity should be present.
What i have read on it and understood as of now is, that OLS is a good method for simple linear regression. Gradient descent is better method when working with many Independent variables. You can understand more on this by watching Josh Stramer's Gradient descent video
why u r keep saying 0.5 again and again sir. This is not cleared. Plz send the link for the reference coz i m watching this playlist step by step but im still not getting this 0.5 criteria
hi krish i had bought your package of 300 rs on youtube , just to get in connect with you .i need your time to answer some of my doubts.Its regarding switching my career into datascience.Please let me know way to connect with you directly
@@krishnaik06 sorry we cant apply dimensionality reduction as its only deal with high variance and has no relation with our class output... we will be using randomized lasso regression or randomized logistic regression.. thank you krish bhai
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Thanks, Krish. One way that you suggested is to Drop the feature.
There is one more thing that we can do is combine the features which we also called them as an interaction term (like multiply Age and years of experience, and then run your OLS model). This also seems to control collinearity to some extent
Now the concept of Multicollinearity is totally clear....thanks bhaiya🙂👍✌️
I was searching for the theory behind backward elimination ....everywhere just the procedure was given but why was explained by you ...Thank you, sir
You are simply awesome... Nothing else to say.. Thank you for your contribution and service...
Hello Krish,
Thanks for the amazing explanation.
I have one question as - Is it necessary to check summary and then conclude about multicollinearity. Can we proceed only with corr() function and conclude about multicollinearity.
Thanks in advance, :)
#multicollinearity #Krish Naik #Regression
@KrishNaik Sir !! What about another technique called as VIF(Variance inflation factor)?That is the technique also used mostly for multicollinearity
Krish, could you explain why AGE has the high P value and not Years of Experience? If you remove Years of Experience from the table, then Age has a P value of 0, as this makes sense as in the data as Age increases, Salary increases accordingly.
Unless you calculate Variance Inflation Factor, you cannot be certain that multicollinearity is nor present. Ideally you should be calculating partial correlation coefficient and compute (1/1-partial correlation coefficient) and see if this is greater than 2
Thank you very much sir for explanation at 00:00
Nice presentation! Dropping a feature is the one solution, sometimes you can’t drop and you may need that feature in the analysis what would you do? Combine features or division or multiplication? Or use factor analysis? Mostly the cases like Marketing Mix modeling the similar issues arise often. Please Try videos like Marketing Mix modeling, Optimization, and forecasting estimating ROI, IROI, response curves....
I think, instead of removing any feature we can combine both correlated features (in this context you talked about)
Age+YearsOfExperience=> Seniority level.
Problem solved. (domain knowledge required for feature engineering).
amazing Explanation
Hi Krish, Nice explaination of the concept. If you could make a video on Assumptions of Linear Regression ( theory and practical) , that would be of great help for us.
Hey Krish, do we really need to look at Std. error? I think looking at p-vaue is enough as p-value is calculated based on t-statistic, which is calculated based on coeff. and std. error. Correct me if I am wrong, thanks
1.Linearity
2.Homoscedascity
3.Multivariate normality
4.Indepence of errors.
5.Lack of multicollinearity.
Please do a video on it.
I am not getting these concepts.
thankyou for such a wonderful explanation sir
Thanks sir your explanation is really good
plotting a correlation matrix and analyzing it is a faster option right?
Yes
Intuition King is back.
Thanks Krish,
Please upload further Deep Learning videos.
Wait if the newspaper column/feature is not correlated then how do we come to the conclusion that it can be dropped as the P-value is high? Are we making the conclusions based on the T-test values here as well?
WAH! beautiful sir..
So that means we should now have 0 expenditure on newspaper? And after removing the newspaper feature, if we do OLS again, can there be any other feature with p-value less than 0.5 in the new OLS model?
An imp ques, if anyone knows, please answer ---> "Why we give OLS, the X value(which is constant) and not the x values(which are our independent features)? if we are not giving the independent values to OLS, then how can it is showing the table in output(with X and y?)"
So to sum up, to identify multicollinearity, we use OLS (ordinary least squares) method to check the model summary for std error (values should be less not high), Rsquare value (should tend towards 1), p value (should
Thankss, good elaboration!!
What is the difference between OLS model and Linear regression with MSE ??......Do both give same ??.... plzz clear it 🙏🙏
Hi Sir,
can we directly find covariance matrix of the feature data and find the determinant of the covariance matrix if it is Zero then we can directly say that our covariance matrix is singular and if it is singular then it having dependent variable. so there is Multi-Collinearity in our data ...
Will this approach work on data with huge features?
Thank you so much
at the last, you said 1 unit decrease in newspaper exp will result in one unit increase in sales.
can you plzz explain it. because according to me it will result in change in profit but not in sales
Good explained. Thanks a lot
can we use the same process for logistic regression brother....?
Please After making a model with python , training, testing and saving it, how can I put new data through it for predictions ? Thanks for the content
for ex if here is how you have done made the regressor:
reg=LinearRegression()
reg.fit(x_train,y_train)
then you can predict it like:
reg.predict([[100]]) and you will get the predicted value
if you have trained for multiple features, then use like reg.predict([[100,200]]) according to the dataframe you have trained!
Is it a good practice to find multicollinearity for other ml models also apart from linear? Is it necessary for other models as well?
I was looking for Multicollinearity problem in interaction terms. How to solve this problem? One suggestion is centring the variable! But still confused! When one of my interaction variables is dichotomous and coding =0 is important for me, then if I use centering, 0 coding will not be there anymore! So how to solve this? Can you please clarify this for me? Thanks
Can you please explain why p values is biased towards in Age variable why it is not for years of experience.
Sir kindly upload vdio on ridge regression for lambda value and how can simulate with respect to the basic estimators or formulas
Why multicollinearity arises?
What happens that reduces adj Rsq?
Can anyone explain why we are adding constant 1 in the predictor variables?
Can we just use principle component analaysis to see if all variance is covered by reducing number of features here?
Is it like hypothesis testing??
In the 2nd example wasn't scaling important??
Cool video, but about heteroskedasticity?
very good
Isn't P value used for null hypothesis??
while Importing this library -> import statsmodels.api as sm, I'm getting this error -> module 'pandas' has no attribute 'Panel'
Sir what is P>[t]
If I remove years of exp instead of age just by correlation matrix. It will work right? I don't have to check for p values as if I can construct a LR model with just age could change the p value? Any thoughts @channel
Hi krish kindly please answer to this question of mine
for under and oversampling data the techniques u have mentioned is applied on numneric datatypes
for categorical datatypes first we have to encode in some 0,1 then further SMOTETomek and neamiss wil apply that is the proper way to deal ??
and if we use trees approach then it will straight away deal this imbalance and encoding thing itself ??
Please krish reply to this question it will be a big help!
Yes tree approach will solve that problem. But understand tree techniques usually require lot of time
@@krishnaik06 sir thanks a lot i m just a beginner follow ur each video thoroughly . Sir can u make a video on how to built ur resume for guys looking for transition in ds.
Please make the topics in order, getting confused
sir why you cannot choose tv as B0.??
thanx a lot sir !
As year and age are related how to decide which one to drop?
what is the p value in above explanation?
Hi bhaiya, didn't get why you choose to drop age feature over year of experience.
because age and experience are highly correlated and we could see the p value of age was greater than 0.05
Sir Can i use PCA for ignoring Multi_ colinearily
Hi Krish,
I got a doubt. If i ignore multicollinearity in the second example, how will it affect the prediction?
Hi Karish, i have one question, which i am facing daily basic in realtime, How to detect heteroscedasticity and what is the method to rectify it in python ?
There are many tests available. You can research on Bartlett's test. It assumes that the samples come from populations having same variances. There is another called Goldfeld quandt test. To rectify in python, what I Believe is that when working with MLR, if the normality assumption is satisfied then, you shouldnt get heteroscedasticity. So if we have variables that aren't following normal distribution, they can be transformed (refer Krish's video for that too) and then I dont think heteroscedasticity should be present.
correlation part 1 video is missing please upload and rearrange with sequence
why you are ignoring age only why can't we ignore years of expereince?
What does the p value mean??
Pls add the link of colinearity
Hi sir, I have a doubt. Could we use this if we have one hot encoded features in the dataset?
Did you get the answer
What is the difference in OLS and gradient descent?
What i have read on it and understood as of now is, that OLS is a good method for simple linear regression. Gradient descent is better method when working with many Independent variables. You can understand more on this by watching Josh Stramer's Gradient descent video
what does p-value signifies?
why u r keep saying 0.5 again and again sir. This is not cleared. Plz send the link for the reference coz i m watching this playlist step by step but im still not getting this 0.5 criteria
When will the next deep learning batch begins
may i know is there a part one of this video?
hi krish i had bought your package of 300 rs on youtube , just to get in connect with you .i need your time to answer some of my doubts.Its regarding switching my career into datascience.Please let me know way to connect with you directly
dekhlia, pr samjh nhi aya agar 150+ columns honge to kaise krna hai!!
use iloc to get independent variables and store it in X and store target in Y and use OLS model
hi i am getting error after running this line "import statsmodels.api as sm"
ImportError: cannot import name 'factorial'
i am beginner so plz help
You need to downgrade your scipy or install statsmodels from master. You can check this in stackoverflow
What if our feature are in 1000's ? pls reply any one who knows can answer
For that we will apply dimensionality reduction
@@krishnaik06 Kabhi socha nahi tha, ki BHAI khud reply denge
@@krishnaik06 sorry we cant apply dimensionality reduction as its only deal with high variance and has no relation with our class output... we will be using randomized lasso regression or randomized logistic regression.. thank you krish bhai
Sorry but have to ask again in my previous comment can someone correct me ??
How is Dell ispiron i5 8th gen 2tb HDD for data science and programming work.
Its pretty enough though you can use google colab for your ML projects
bhai data set to de dia kro
everything is fine Krish sir but pls try not to say particular in every sentence...it is very annoying
your voice is not that much clear