In Naive Bayes , Outliers will affect the shape of Gaussian distribution. That is the Outliers would have impact on the mean and standard deviation of the bell curve., So Naive Bayes is sensitive to Outliers.
Yes @KrishNaik Sir KNN is sensitive to outliers and Imbalanced dataset which was said by you in the video of KNN In Depth Intuition Video Thanks & Regards, CHINMAY N BHAT
KNN may be affected by outliers , but weighted knn is not for sure as weight proportional to 1/distance & for outlier , it has abnormal high distance from rest of the dataset , so weight is going to be very less & as a result , outliers will be less impactive :-)
@@sajidchoudhary1165 Yes , I know well why we use weighted knn , I was telling , if we go with weighted knn , then the outlier can not affect that much on prediction as you said also weights are use for giving importance to nearest points , so outliers will get less importance
KNN may be affected by outliers , but weighted knn is not for sure as weight proportional to 1/distance & for outlier , it has abnormal high distance from rest of the dataset , so weight is going to be very less & as a result , outliers will be less impactive :-)
Sir , I have one doubt regarding the Robustness of Regression Tree towards Outliers. Regression tree split criterium depends on the averages of the two groups that are splitted, and, as the average is severely affected by outliers, then the regression tree will suffer from outliers. Whereas Classification Tree uses Mode which in not affected by outliers ... So when we say "Decision trees are robust to outliers", we mean Decision Tree Classifier , not Regressor. So I think Regression Tree are affected by outliers :-/
can u please help I need to know if LPNT is a reliable cryptocurrency as the Probit exchange shows its status as Outlier? please give it a glance as I have invested a lot of money in it.
i also think so KNN Classifier is sensitive to outliers and you also told in one video KNN Classifier is Sensitive and DBSCAN is'nt sensitive to outliers because it handle outliers by itself it don't consider outliers in Clusters
mean is highly affected by outliers , like for example , mean of 1,2,3,4,6,10000 is 1669.33 something , that means one single outlier is affecting the mean such that , the mean is not representing the majority part of the dataset properly , so when outlier is present , you should never use mean , rather median is not that much affected by outliers , you can try that or some other advanced algorithm , hope this will be helpful ! Happy Learning :-)
In I am doing EDA , as per discussion EDA will be done on raw data, in raw data many outliers and we may or may not made decision on outliers, before doing eda , we need treat outliers ..please clarify ...Thanks
with due respect sir one of the videos of KNN you told that KNN is sensitive to outliers but you have written that KNN is not sensitive to outliers, it makes me more confusing. sir kindly please clarify PS huge fan of yours
KNN may be affected by outliers , but weighted knn is not for sure as weight proportional to 1/distance & for outlier , it has abnormal high distance from rest of the dataset , so weight is going to be very less & as a result , outliers will be less impactive :-)
mean is highly affected by outliers , like for example , mean of 1,2,3,4,6,10000 is 1669.33 something , that means one single outlier is affecting the mean such that , the mean is not representing the majority part of the dataset properly , so when outlier is present , you should never use mean , rather median is not that much affected by outliers , you can try that or some other advanced algorithm , hope this will be helpful ! Happy Learning :-)
hello sir, i have one query. may be not relevent to this.. but if my data is not normally distributed and if wants to apply gussian naive bayes what method can be apply to convert data into normally distributed
Hello , can some one please answer...weather i can consider a categorical variable in which one class has very low frequency as compared to other classes as an outlier????
Sir i want to transit my current sofware domain to data science domain. Enrolling in aplied ai course for total data science is it ok or this course will only teach the ai and ml?
Not Sure about this video everywhere i serached it says naive bayes is sensitive to outliers even SVM and linear regression already so much confusion in Data science topic and this is more confusing and misguiding
In Naive Bayes , Outliers will affect the shape of Gaussian distribution. That is the Outliers would have impact on the mean and standard deviation of the bell curve., So Naive Bayes is sensitive to Outliers.
Love u Krrish Sir always been ur fan.
Data science expert
Excellent krish. God bless you. Keep working like this
Yes @KrishNaik Sir KNN is sensitive to outliers and Imbalanced dataset which was said by you in the video of KNN In Depth Intuition Video
Thanks & Regards,
CHINMAY N BHAT
KNN may be affected by outliers , but weighted knn is not for sure as weight proportional to 1/distance & for outlier , it has abnormal high distance from rest of the dataset , so weight is going to be very less & as a result , outliers will be less impactive :-)
@@KnowledgeAmplifier1 weights don't used for handle outliers , weights are use for giving importance to nearest points
yes , he said
@@sajidchoudhary1165 Yes , I know well why we use weighted knn , I was telling , if we go with weighted knn , then the outlier can not affect that much on prediction as you said also weights are use for giving importance to nearest points , so outliers will get less importance
@@KnowledgeAmplifier1 what if outliers are much nearer toh future data point
thank you mister krish
Thanks Krish! Really nice video!
Excellent Krish sir
KNN may be affected by outliers , but weighted knn is not for sure as weight proportional to 1/distance & for outlier , it has abnormal high distance from rest of the dataset , so weight is going to be very less & as a result , outliers will be less impactive :-)
KNN
if k=low, the model is susceptible to outliers
if k=high, robust to outliers
Excellent sir
Sir , I have one doubt regarding the Robustness of Regression Tree towards Outliers.
Regression tree split criterium depends on the averages of the two groups that are splitted, and, as the average is severely affected by outliers, then the regression tree will suffer from outliers.
Whereas Classification Tree uses Mode which in not affected by outliers ...
So when we say "Decision trees are robust to outliers", we mean Decision Tree Classifier , not Regressor.
So I think Regression Tree are affected by outliers :-/
Hello, Why no tuse a multivariate outliers detector instead of univariate analysis?
Sir, DBSCAN is not sensitive to outlier, it is actually robust to outlier.
SVMs are sensitive to outliers the support vectors are calculated based on distance only
why can't we apply transformation techniques to remove skewness and verifying using Q-Q plot?
did you get your answer?
Is it advisable to use Log scaling on target variable to over come outlier?
Yes
What is log scaling
can u please help I need to know if LPNT is a reliable cryptocurrency as the Probit exchange shows its status as Outlier? please give it a glance as I have invested a lot of money in it.
please
i also think so KNN Classifier is sensitive to outliers and you also told in one video KNN Classifier is Sensitive and DBSCAN is'nt sensitive to outliers because it handle outliers by itself it don't consider outliers in Clusters
finished watching
krish unfortunately u did one mistake here. Actually KNN is very sensitive to outliers. Guys u can google it.
sir if we have 80 columns and 5 rows and how to find outliers for each every columns and how to remove it sir
use for loop, and function defination....
What of applying log1p to the skewed distribution?
What's wrong sir at 1:31:54 ??
😂🤣😁 he forgot he is teaching.
could you please explain why you replaced outliers with upper bound value? can we replace it with mean?
mean is highly affected by outliers , like for example , mean of 1,2,3,4,6,10000 is 1669.33 something , that means one single outlier is affecting the mean such that , the mean is not representing the majority part of the dataset properly , so when outlier is present , you should never use mean , rather median is not that much affected by outliers , you can try that or some other advanced algorithm , hope this will be helpful ! Happy Learning :-)
if distribution is left skewed then we have to replace all left hand values less than -3S.D. with -3S.D. to make it normally distributed?
What if in multinomial naive Bayes , you have given a test data containing a word which is not in our document matrix as a feature
In I am doing EDA , as per discussion EDA will be done on raw data, in raw data many outliers and we may or may not made decision on outliers, before doing eda , we need treat outliers ..please clarify ...Thanks
Please discuss MATLAB Coding as you have discussed python coding
i am working on covid 19 prediction.. should it is needed to remove outliers or not.
if the outliers are genuine you should not remove them it depends on the business use case too
Sir I have one doubt. Is there a difference between min value and lower bridge? For fare my min value shows zero but lower bridge shows negative 61.
with due respect sir
one of the videos of KNN you told that KNN is sensitive to outliers but you have written that KNN is not sensitive to outliers, it makes me more confusing.
sir kindly please clarify
PS huge fan of yours
knn actually is really sensitive to outliers you can easily see that as it is fully based on norms
KNN may be affected by outliers , but weighted knn is not for sure as weight proportional to 1/distance & for outlier , it has abnormal high distance from rest of the dataset , so weight is going to be very less & as a result , outliers will be less impactive :-)
sir can we replace outliers with mean value of that variable? or it will cause any error or something , if we have large data and lots of outliers.
mean is highly affected by outliers , like for example , mean of 1,2,3,4,6,10000 is 1669.33 something , that means one single outlier is affecting the mean such that , the mean is not representing the majority part of the dataset properly , so when outlier is present , you should never use mean , rather median is not that much affected by outliers , you can try that or some other advanced algorithm , hope this will be helpful ! Happy Learning :-)
Sir can u explain travelling salesman
Hey @Krish Naik do we have to make end-to-end projects just like you or make custom functions for steps. Which looks more professional Please reply.
How about outlier analysis in text data ?. Is there any specific method Krish
hello sir, i have one query. may be not relevent to this.. but if my data is not normally distributed and if wants to apply gussian naive bayes what method can be apply to convert data into normally distributed
Hello , can some one please answer...weather i can consider a categorical variable in which one class has very low frequency as compared to other classes as an outlier????
Is it possible to apply RNNs for the Outlier Detection?
print('Thanks a lot, Krish !)
finished practicing code
If the dataset contains more 30% of outliers , how to impute them ...( if we cannot drop also and not able to replace with mean/mode/ median)
Sir i want to transit my current sofware domain to data science domain. Enrolling in aplied ai course for total data science is it ok or this course will only teach the ai and ml?
Hi sushovan, you have asked the wrong window. do to applied ai channel and ask. you may get answer
Where do we get materials
Can 3 quartiles(20%, 50% and 75%) be the same?
Hey, make an unboxing video of titan rtx please!!
Coming up soom
Hi sir
KNN is sensitive sir
SVM is sensitive to outliers.
Not Sure about this video everywhere i serached it says naive bayes is sensitive to outliers even SVM and linear regression already so much confusion in Data science topic and this is more confusing and misguiding
Hii