@@tanvishinde805 He's considering sent1 as an example and not that BOW matrix. In sent1 three words are present, so only those features would be considered to calculate the probability. If sent has both delicious and bad words present then it won't give definitive o/p, which he should've explained.
Nice explanation Krish. Can you explain 1.How do we solve new word (taste) is presented in the existed sentence (The food is Delicious). 2.What will happen Dataset is imbalanced. You mentioned like will upload this two problems in next video .I did not find that video,please upload Krish. Thank you
I came across a concept called Laplace smoothing which helps how to deal when test data contains a new word which is not available in Training Data Set. This might help you.
The calculation of conditional probabilities are wrong. P(x2|y=yes) = 2/2 not 2/4. There are 2 yes values, hence denominator is 2. Out of 2 yes values, both have x2 and so numerator is 2.
@shawn chung yes,i think so , because p(x2) given that yes will be 2/2(we have two yes and both are having x2=1) not 2/4. please correct me if my assumption is wrong.
@@uttamchoudhary5229 Actually it will be p(x2 | y=yes) = no. of words (x2) / total no. of words in Class Y = Yes no. of words (x2) in yes class= 2 total no. of words in Class( Y = Yes) = 5 So it should be = (2/5) = 0.4
yeah you are correct, you can also see in this way that p(X2/y=yes) is basically -> number of times word X2 appear given output is 'yes'/ number of times 'yes' appear.
I think P(x2 | Y=yes) should be 1 ... Same goes for the other cases x3. the sample space given y=yes should only count events where y=1.... Please Correct me if I am wrong....
I believe probability of No|Sentence-1 is zero due to the word delicious. So after normalization P(Yes|Sentence-1) is 100% and not 70-80. Please correct me if required.
@@srinivasarukonda8768 YES, but if u calculate, P(The/yes)=1/2, P(Food/YES)=2/2, P(delicious/Yes)=2/2; Bcoz formulae for P(A/B)=P(A intersection B)/P(B) ; So the final answer will be 1/5. Can anybody confirm this?
Thank you for the video. Do you have Tutorial 50. I mean the next part explaining what if when the data set is imbalanced. Where does Naiye Bayes fail ?
sir according to me P(x2/y)= P( x2 ^ y)/P(y) , where P(x2^ y) = how many time x2 = 1 when out output is 1 = 2, and P(y) is how many times we are getting output as 1. Therefore P(x2/y) = 2/2= 1, nor 2/4. correct me if I am wrong
Hi krish, Your videos are really amazing and been following you since the start of my ML study. can you upload the video about how to solve the problem of imbalanced datasets and also whenever the new word is present in unkown dataset i.e when probability becomes zero.? thankyou!
Awesome video Krish, I feel the condition probability calculation is not correct. I've read some blogs and watched other videos there their method is different. and they all are the same, I referred AAIC, codebasis, and some blogs from analytics Vidhya and medium.
i want a video on this too ...How To Apply Decision Tree' Classifier On Text Data (NLP)- Machine Learning.. on naive bayes it is easy but on dt i was confused.. pls help maae baap New Intro is awesome
I wounder why last feature was not calulated or explained. what will be the probabilty of (Bad|yes)? Is it 0? If yes, the whole answer will become zero
Sir it would be greatful of you, if you make a video explaining the output of all clasifiers and regressors. I mean, SVM, naive bais, logistic all returns coefficients. Its hugely confusing similar in regression aswell. It will great if you address this. You are the last hope sir
I don't understand why isn't the queries below are being addressed. Seems like the video was made hurryingly and all the calculation and concepts are messed up.
Hello Krish, Is likelihood the same as the probability for discrete variables? Here we are substituting the likelihood with the probability of the word in the Naive Bayes but when a continuous variable eg. income is an independent variable then we calculate the mean and sd to find the likelihood for that particular point in the distribution. So the confusion arises when we talk about categorical aka discrete variables we can interchange the terms probability and likelihood as it means the same. Kindly help
@krish Naik, Thanks for the nice explanation. I have this doubt, why we always apply Multinomial NB for text classification, why not binomial or Gaussian NB. could you please explain ?
I learn naive Bayes before also,,, but with the real life use case I understand ,,,,,I have a question Krish sir ,,,how you take the values in the table (like 0,1),,,pls clear my doubt ,,,
1 - There are problems in calculation of probabilities, for example p(X2/y=yes) and others. Please fix them, because it causes misunderstanding for all people who watch this video. 2 - 1/10 is 0.1 not 0.01 3 - For a positive sentence, how the probability of yes (25%) is less than no(75%) ?
I am not able to find the next part about imbalanced dataset and how to deal with the drawback plz anyone can send the link ?? Also what if i have multi class dataset ??
The explanation is wrong Sir. Please correct it and upload a new video on this. It will misguide a lot of students.P(x2|y=yes) = 2/2 not 2/4. There are 2 yes values, hence denominator is 2. Out of 2 yes values, both have x2 and so numerator is 2.
We are calculating y=yes for the sentence 'The food is delicious' where after pre processing(strop words removal) the sentence becomes 'The food delicious' and the BOW is applied to get the feature matrix (chart ) shown in the video. Remember, BOW makes a feature matrix of all words in the data.Since we have 3 sentences and the word bad is also present, thus it will be there in the chart. however, when we are finding y= yes for sentence '"The food delicious" we wont include word ''bad' in the calculation as its not part of the sentence. If u want to calculate y= yes for the second sentence 'The food is bad' which after preprocessing becomes ''The food bad' then we wont calculate the value of delicious. Hope this clears your doubt. In case u are confused check out any TH-cam video for Bag of Words and stopwords removal.
Sir at 9:02 it should be 2/2 and not 2/4. Because we have to consider only yes cases. Please clarify if I am wrong.
yes
you are right
i thought the same
yes even I think the same
Its correct. 2 times yes when food is present / total # of times food is present
yes i think the same , but in that case P(Bad | yes ) = 0 and so the whole term P(yes | sentence1) = 0 , so this should not happen
@@tanvishinde805 He's considering sent1 as an example and not that BOW matrix. In sent1 three words are present, so only those features would be considered to calculate the probability.
If sent has both delicious and bad words present then it won't give definitive o/p, which he should've explained.
Nice explanation Krish. Can you explain
1.How do we solve new word (taste) is presented in the existed sentence (The food is Delicious).
2.What will happen Dataset is imbalanced.
You mentioned like will upload this two problems in next video .I did not find that video,please upload Krish. Thank you
I came across a concept called Laplace smoothing which helps how to deal when test data contains a new word which is not available in Training Data Set. This might help you.
Your videos are really amazing. A quick note: P(1|yes) = 0.1 (10%) :)
The calculation of conditional probabilities are wrong. P(x2|y=yes) = 2/2 not 2/4. There are 2 yes values, hence denominator is 2. Out of 2 yes values, both have x2 and so numerator is 2.
Thanks bro, you are right :)
@@aryantyagi2189 🧐
no. food =4
food/yes ,, =2
p(food/yes)=2/4.... i think its right
@@santhiyaA-sz8db no man it's conditional probability ,when P(A/B) you go look for the portion of A is true when B is true not overall
Hi Sir, isn't p(x2 | y=yes) = 2/2= 1? not 2/4? (9:02)
because p(food=1 | y=yes) = p(food=1, y=1)/ p(y=1) = ( 2/5 ) / (2/5) = 1
@shawn chung yes,i think so ,
because p(x2) given that yes will be 2/2(we have two yes and both are having x2=1) not 2/4.
please correct me if my assumption is wrong.
@@uttamchoudhary5229
Actually it will be p(x2 | y=yes) = no. of words (x2) / total no. of words in Class Y = Yes
no. of words (x2) in yes class= 2
total no. of words in Class( Y = Yes) = 5
So it should be = (2/5) = 0.4
@@ppsheth91 There are only 3 unique words where Class( y = yes ). It should be 2/3
8:24 p(the/yes) =number of times the==1 when o/p==1 also for p(food/yes)=2/2
you are right brother
Sir, I have a doubt. When we are calculating p(X2/y=yes), should it not be equal to p(X2 intersection y=yes) / p(yes) = 2/2?
yes , your explanation is correct
yes, you are correct
yeah you are correct, you can also see in this way that p(X2/y=yes) is basically -> number of times word X2 appear given output is 'yes'/ number of times 'yes' appear.
That's right. It should be 2/2 i.e. 1.
Yes you are correct.
I think P(x2 | Y=yes) should be 1 ... Same goes for the other cases x3. the sample space given y=yes should only count events where y=1.... Please Correct me if I am wrong....
it was good content, thanks for making such video, its really nice to learn thing like this
For word 'bad' , will its conditional probability be zero and will make whole probability zero since 0*anything is 0 ?
eagerly waiting for NLP series... btw your work is amazing. Thank you!
how can i find the next video, the youtube doesn't recommend :(. thank you for ur work, really clear
thank you sir ur videos has helped me a lot and i can't thank u enough for the great work that u r doing
We calculate the probability of xi given y=1 this means we filter by y=1 and then calculate the probability. So, P(x2/y=1)=2/2 etc
I believe probability of No|Sentence-1 is zero due to the word delicious. So after normalization P(Yes|Sentence-1) is 100% and not 70-80. Please correct me if required.
you are correct
@@srinivasarukonda8768 YES, but if u calculate, P(The/yes)=1/2, P(Food/YES)=2/2, P(delicious/Yes)=2/2; Bcoz formulae for P(A/B)=P(A intersection B)/P(B) ; So the final answer will be 1/5. Can anybody confirm this?
@@pradeep611 P(Food/YES) IS 2/4
yes but why he used 0.03 and also it will be 0.1 not 0.01
There is a calculation mistake. 1/10= .1 and not .01 . That's why he got the wrong result initially.
Decimal power goes off as we do normalize. so results should be ok.
Wrongly calculated the probabilities P(x1|yes) and others. Anyhow great content
Thank you for the video. Do you have Tutorial 50. I mean the next part explaining what if when the data set is imbalanced. Where does Naiye Bayes fail ?
Thank you for this video. Can you please share videos for the implementation of these concepts using python also?
sir according to me P(x2/y)= P( x2 ^ y)/P(y) , where P(x2^ y) = how many time x2 = 1 when out output is 1 = 2, and P(y) is how many times we are getting output as 1. Therefore P(x2/y) = 2/2= 1, nor 2/4. correct me if I am wrong
Yes..he is explaining wrong actually
Waiting for NLP series eagerly....
Hi krish, Your videos are really amazing and been following you since the start of my ML study.
can you upload the video about how to solve the problem of imbalanced datasets and also whenever the new word is present in unkown dataset i.e when probability becomes zero.?
thankyou!
Thank you sir, my concepts got cleared
Awesome video Krish, I feel the condition probability calculation is not correct. I've read some blogs and watched other videos there their method is different. and they all are the same, I referred AAIC, codebasis, and some blogs from analytics Vidhya and medium.
i want a video on this too ...How To Apply Decision Tree' Classifier On Text Data (NLP)- Machine Learning.. on naive bayes it is easy but on dt i was confused.. pls help maae baap
New Intro is awesome
Superb explanation. Thanks a lot Krish
great tutorial..
Thank you sir... Great work sir... 👍👍👍👍🙏🙏🙏
great content keep doing
The video is good, but noticed few things :
1. "The" is also a stopword. 2. it's 25% and not 0.25%
I wounder why last feature was not calulated or explained. what will be the probabilty of (Bad|yes)? Is it 0? If yes, the whole answer will become zero
Sir, where is that next video related to the problems in the Naive Bayes theorem I really want that and don't want to lose the movement.
GOOD CONTENT TY...
Sir it would be greatful of you, if you make a video explaining the output of all clasifiers and regressors. I mean, SVM, naive bais, logistic all returns coefficients. Its hugely confusing similar in regression aswell. It will great if you address this. You are the last hope sir
I don't understand why isn't the queries below are being addressed. Seems like the video was made hurryingly and all the calculation and concepts are messed up.
thanks for this awesome tutorial. Hats off to you sir.
Would you please make video on Support Vector machines with its mathematical concepts...
Yes
thank you sir
Great Explanation sir when will you post Tutorial 50
on to deal with word which is not present in the training dataset
Excellent explanation 👌
Good video. Wrong calculations on p(x1|y=yes) though
p(x1/y=yes) should be 2/2 =1
Well explained :) Do you have python implementation of same example ? Naive bayes implementation without library ?
Hello Krish,
Is likelihood the same as the probability for discrete variables?
Here we are substituting the likelihood with the probability of the word in the Naive Bayes but when a continuous variable eg. income is an independent variable then we calculate the mean and sd to find the likelihood for that particular point in the distribution. So the confusion arises when we talk about categorical aka discrete variables we can interchange the terms probability and likelihood as it means the same. Kindly help
please solve P(y=no/sentence) there is some problem in it
nice video as usual
Probability of the No will Be 0 because of delicious features probability is 0
You're the best!
Thanks
P(y=No|Sent1)=3/5 * 2/3 * 1/3 * 3/3 = 0.13 is this computation correct?
@krish Naik, Thanks for the nice explanation. I have this doubt, why we always apply Multinomial NB for text classification, why not binomial or Gaussian NB. could you please explain ?
Really helpful❤
hi krish... in the example shown, you computed P(y=yes|sentence)..... shouldnt this sentence be a query sentence and not one of the training sentence?
I learn naive Bayes before also,,, but with the real life use case I understand ,,,,,I have a question Krish sir ,,,how you take the values in the table (like 0,1),,,pls clear my doubt ,,,
the is also stop words ?
Which input taken to predict the early reviewers by using navie bayes ??
Sir please tell how to implement it practically
How did you calculate output column in BOW step? If we don't have that column then in that case how we will proceed?
Sir please also add code portion
I am confused
First p(y=yes) is 0.1
second p(y=no) is 0
now after normalization, we get 1 for yes and 0 for No...
correct if i am wrong
Sir how do we do it on Image data... if we have pixels as feature of our data set..how can we find the P(features/Class=k) ??
I don't understand, how you choose this feature table.
well explained
Sir very good video. Will it be possible to make video based on naive bayes using TF-IDF processed data
At 10:00 shouldn't we also take p( x4 | y=yes) ??
Sir, why are you not taking the probability of word 'bad', when you are computing the probability for yes.
He's only showing us Sentence1 which doesn't have the word 'bad'.
The p(x=food| yes) =2/4 is correct?
But "yes" is apparent just 2 times not 4!!!! Or im wrong??
sir....plz upload video on how to solve the problem when the naive Bayes is fail
P(y=no/sentence)=0.15
.Please check.
1 - There are problems in calculation of probabilities, for example p(X2/y=yes) and others. Please fix them, because it causes misunderstanding for all people who watch this video.
2 - 1/10 is 0.1 not 0.01
3 - For a positive sentence, how the probability of yes (25%) is less than no(75%) ?
Great content!
Boss..It is not Bayes theorem formula.But Everyone are saying the same.The end calculation giving us right Bayes theorem.
sir would you like to share tutorial 50 you were supposed to share and would you please arrange machine learning playlist according to order
Sir I am unable to find ur naive bayes video after 49th one in machine learning.. Please upload it..
why is f4 /x4, "Bad" not taken for p(yes/sentence)
Could you please make a video on AB testing
great sir
1/10 is 0.01? but everything else makes sense. ty for sharing man.
Thanks krish
Can you please explain how to predict same for new sentence
9:30 guys it 0.1 not 0.01
dude amma see them all though i know nothing about programming
I am not able to find the next part about imbalanced dataset and how to deal with the drawback plz anyone can send the link ?? Also what if i have multi class dataset ??
Can you please share the next part of this video ??
how to calculate when output is not there for other sentences?
Where is the next video ? I mean the next part of naive Bayes after this
The explanation is wrong Sir. Please correct it and upload a new video on this. It will misguide a lot of students.P(x2|y=yes) = 2/2 not 2/4. There are 2 yes values, hence denominator is 2. Out of 2 yes values, both have x2 and so numerator is 2.
Yes yes
beautiful
Which is the common tool for data science
good ()great) explanation but ... Calculation mistake !!!
How to use this algoritham in digital marketing?
So many calculation error and wrong probabilities taken
Where did 0.03 come from? @10:50
lot of errors Krish. Good video though.
9:24 why P(Bad | Yes) is not considered in this calculation ?
We are calculating y=yes for the sentence 'The food is delicious' where after pre processing(strop words removal) the sentence becomes 'The food delicious' and the BOW is applied to get the feature matrix (chart ) shown in the video. Remember, BOW makes a feature matrix of all words in the data.Since we have 3 sentences and the word bad is also present, thus it will be there in the chart. however, when we are finding y= yes for sentence '"The food delicious" we wont include word ''bad' in the calculation as its not part of the sentence.
If u want to calculate y= yes for the second sentence 'The food is bad' which after preprocessing becomes ''The food bad' then we wont calculate the value of delicious. Hope this clears your doubt.
In case u are confused check out any TH-cam video for Bag of Words and stopwords removal.
@@MindScape322 oh yes! Thank you
Gibbs algorithm?
can anyone send link of next video that mean 50
do videos in kannada also
p(yes/sent1)=1/10=0.1
p(no/sent1)=0( delicious 0/2) 0* anythink..=0
p(yes)=.1/0+.1
=.1/0.1
=1
p(no)=1- p(yes)
=1-1
=0
is correct ?
where is tutorial 50?
BA student can become data scientist?
anyone.. but working hard is the key.
Can anyone help me finding the tutorial -50