Tutorial 49- How To Apply Naive Bayes' Classifier On Text Data (NLP)- Machine Learning

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Tutorial 49- How To Apply Naive Bayes' Classifier On Text Data (NLP)- Machine Learning

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Krish Naik

Күн бұрын

Пікірлер: 148

@Waurice 3 жыл бұрын

Your videos are really amazing. A quick note: P(1|yes) = 0.1 (10%) :)

@hemantdas9546 4 жыл бұрын

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.

@sonusingh-hj2dw 4 жыл бұрын

yes you are right i thought the same

@pavanidubey3399 4 жыл бұрын

yes even I think the same

@siddharthdedhia11 4 жыл бұрын

Its correct. 2 times yes when food is present / total # of times food is present

@tanvishinde805 3 жыл бұрын

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

@sarsizchauhan8948 3 жыл бұрын

@@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.

@premtejak 4 жыл бұрын

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

@vinaychitturi5183 3 жыл бұрын

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.

@user-ni3uw5cc7u 7 ай бұрын

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....

@rahulsaha4728 4 жыл бұрын

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.

@aryantyagi2189 Жыл бұрын

Thanks bro, you are right :)

@atharavmahajan8202 Жыл бұрын

@@aryantyagi2189 🧐

@santhiyaA-sz8db 5 ай бұрын

no. food =4 food/yes ,, =2 p(food/yes)=2/4.... i think its right

@abdelerahmanekhaldi6228 3 ай бұрын

@@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

@soowoonchung5607 4 жыл бұрын

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

@uttamchoudhary5229 4 жыл бұрын

@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.

@ppsheth91 4 жыл бұрын

@@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

@piyushtalreja6855 3 жыл бұрын

@@ppsheth91 There are only 3 unique words where Class( y = yes ). It should be 2/3

@ashwinshetgaonkar6329 2 жыл бұрын

8:24 p(the/yes) =number of times the==1 when o/p==1 also for p(food/yes)=2/2

@mini22q11 3 ай бұрын

you are right brother

@dipanwitamitra3029 3 жыл бұрын

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?

@santhoshreddy9161 3 жыл бұрын

yes , your explanation is correct

@dhruvgrover7416 3 жыл бұрын

yes, you are correct

@mohdkashif7295 3 жыл бұрын

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.

@ltoco4415 2 жыл бұрын

That's right. It should be 2/2 i.e. 1.

@garvitgupta13 2 жыл бұрын

Yes you are correct.

@mandeepsinghnegi1931 4 жыл бұрын

eagerly waiting for NLP series... btw your work is amazing. Thank you!

@RanveerSingh-sp3uj 4 жыл бұрын

it was good content, thanks for making such video, its really nice to learn thing like this

@Satyam-ic4tl Жыл бұрын

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

@lamnguyentrong275 3 жыл бұрын

how can i find the next video, the youtube doesn't recommend :(. thank you for ur work, really clear

@mrfolk84 3 жыл бұрын

For word 'bad' , will its conditional probability be zero and will make whole probability zero since 0*anything is 0 ?

@ARSH_DS007 4 жыл бұрын

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 4 жыл бұрын

you are correct

@pradeep611 3 жыл бұрын

@@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?

@sudhirkv8292 3 жыл бұрын

@@pradeep611 P(Food/YES) IS 2/4

@arpita0608 Жыл бұрын

yes but why he used 0.03 and also it will be 0.1 not 0.01

@rahulm774 4 жыл бұрын

There is a calculation mistake. 1/10= .1 and not .01 . That's why he got the wrong result initially.

@maheshmec1 3 жыл бұрын

Decimal power goes off as we do normalize. so results should be ok.

@vishalvanpariya1466 6 ай бұрын

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.

@sumitkumar79598 Жыл бұрын

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

@angiramajumder1790 Жыл бұрын

Yes..he is explaining wrong actually

@VikashKumar-ty6uy 4 жыл бұрын

Waiting for NLP series eagerly....

@jinal0217 3 жыл бұрын

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 ?

@badrlakhal5440 2 ай бұрын

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

@deepthisudhakaran6417 3 жыл бұрын

Thank you for this video. Can you please share videos for the implementation of these concepts using python also?

@zohaibramzan6381 3 жыл бұрын

Wrongly calculated the probabilities P(x1|yes) and others. Anyhow great content

@krishj8011 2 ай бұрын

great tutorial..

@ManashreeKorgaonkar Жыл бұрын

Thank you sir, my concepts got cleared

@sandipansarkar9211 3 жыл бұрын

Superb explanation. Thanks a lot Krish

@devdaskamath975 4 жыл бұрын

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!

@nandeesh_2005 11 ай бұрын

Excellent explanation 👌

@darpansalunke1729 3 жыл бұрын

Thank you sir... Great work sir... 👍👍👍👍🙏🙏🙏

@codingquiz 4 жыл бұрын

great content keep doing

@sushilchauhan2586 4 жыл бұрын

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

@rajuneelakantam8099 4 жыл бұрын

GOOD CONTENT TY...

@abhijitkunjiraman6697 Жыл бұрын

You're the best!

@winyourself553 3 жыл бұрын

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.

@shekhargaikwad5767 2 жыл бұрын

Great Explanation sir when will you post Tutorial 50 on to deal with word which is not present in the training dataset

@sunnysavita9071 4 жыл бұрын

nice video as usual

@sudhirkv8292 3 жыл бұрын

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

@manthanrathod1046 2 жыл бұрын

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.

@shakeelahmed8624 3 жыл бұрын

Well explained :) Do you have python implementation of same example ? Naive bayes implementation without library ?

@yukeshnepal4885 4 жыл бұрын

thanks for this awesome tutorial. Hats off to you sir. Would you please make video on Support Vector machines with its mathematical concepts...

@krishnaik06 4 жыл бұрын

Yes

@yukeshnepal4885 4 жыл бұрын

thank you sir

@Vinay1272 Жыл бұрын

Really helpful❤

@siddaramhalli 3 жыл бұрын

The video is good, but noticed few things : 1. "The" is also a stopword. 2. it's 25% and not 0.25%

@pratheeeeeesh4839 4 жыл бұрын

Great content!

@skc1995 4 жыл бұрын

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

@fro4e 2 жыл бұрын

Good video. Wrong calculations on p(x1|y=yes) though

@mansidnailartist 2 жыл бұрын

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?

@vincentdepaulsavarimuthu779 2 жыл бұрын

well explained

@rajeshdoolla8623 4 жыл бұрын

@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 ?

@osamaosama-vh6vu Жыл бұрын

Thanks

@mayank113463 4 жыл бұрын

the is also stop words ?

@shadabmathematics9672 3 жыл бұрын

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 ,,,

@amitpingale8247 3 жыл бұрын

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

@divyark7557 2 жыл бұрын

P(y=No|Sent1)=3/5 * 2/3 * 1/3 * 3/3 = 0.13 is this computation correct?

@webdeveloper9704 3 жыл бұрын

great sir

@AjithlalK 4 жыл бұрын

Thanks krish

@swarupgorai 4 жыл бұрын

please solve P(y=no/sentence) there is some problem in it

@arpita0608 Жыл бұрын

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

@hamareshsaivarma6720 Жыл бұрын

Which input taken to predict the early reviewers by using navie bayes ??

@vedmodikauratg1865 3 жыл бұрын

Sir very good video. Will it be possible to make video based on naive bayes using TF-IDF processed data

@elhadigasmi3122 3 жыл бұрын

The p(x=food| yes) =2/4 is correct? But "yes" is apparent just 2 times not 4!!!! Or im wrong??

@sonalmaheshwari8222 4 жыл бұрын

Sir please tell how to implement it practically

@rakshitsinha4392 2 жыл бұрын

At 10:00 shouldn't we also take p( x4 | y=yes) ??

@poojayadav-pq6rd 3 жыл бұрын

How did you calculate output column in BOW step? If we don't have that column then in that case how we will proceed?

@vipingautam9501 2 жыл бұрын

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) ??

@ishtiakahmed3272 4 жыл бұрын

sir would you like to share tutorial 50 you were supposed to share and would you please arrange machine learning playlist according to order

@mohammadsalman2145 3 жыл бұрын

Sir, why are you not taking the probability of word 'bad', when you are computing the probability for yes.

@pitchthewoo 3 жыл бұрын

He's only showing us Sentence1 which doesn't have the word 'bad'.

@dhainik.suthar 3 жыл бұрын

Sir please also add code portion

@hardikaggarwal446 4 жыл бұрын

beautiful

@vanitapatel6391 3 жыл бұрын

sir....plz upload video on how to solve the problem when the naive Bayes is fail

@shiv9475 4 жыл бұрын

Probability of the No will Be 0 because of delicious features probability is 0

@louerleseigneur4532 3 жыл бұрын

I don't understand, how you choose this feature table.

@harendrakumar7647 4 жыл бұрын

Could you please make a video on AB testing

@deepthib7588 2 жыл бұрын

why is f4 /x4, "Bad" not taken for p(yes/sentence)

@Miles2Achieve 4 жыл бұрын

Can you please explain how to predict same for new sentence

@pawankumar.a8451 3 жыл бұрын

Sir I am unable to find ur naive bayes video after 49th one in machine learning.. Please upload it..

@maheshenumula9473 4 жыл бұрын

Boss..It is not Bayes theorem formula.But Everyone are saying the same.The end calculation giving us right Bayes theorem.

@kumarraju2923 4 жыл бұрын

Which is the common tool for data science

@subhrajeetbiswal6942 Жыл бұрын

p(x1/y=yes) should be 2/2 =1

@umeshsuggala3932 4 жыл бұрын

how to calculate when output is not there for other sentences?

@rickymehra109 4 жыл бұрын

Can you please share the next part of this video ??

@user-tg3tg9gh3q 2 жыл бұрын

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%) ?

@harshitvishwakarma310 3 жыл бұрын

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 ??

@deekshas5737 3 жыл бұрын

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.

@anilkumargupta5844 9 ай бұрын

Yes yes

@amitbudhiraja7498 2 жыл бұрын

Where is the next video ? I mean the next part of naive Bayes after this

@Kickass3131 3 жыл бұрын

Where did 0.03 come from? @10:50

@kalyanputatunda5806 21 күн бұрын

P(y=no/sentence)=0.15 .Please check.

@poppychan1953 3 жыл бұрын

dude amma see them all though i know nothing about programming

@akshayakki3631 3 жыл бұрын

How to use this algoritham in digital marketing?

@anirudhbalaji1042 4 жыл бұрын

9:30 guys it 0.1 not 0.01

@MercyGraceThomas 4 жыл бұрын

EM algorithm?

@chenlou7783 Жыл бұрын

1/10 is 0.01? but everything else makes sense. ty for sharing man.

@reeteshkesarwani8960 4 жыл бұрын

sir what is written in tatoo in your hand??

@tanvishinde805 3 жыл бұрын

9:24 why P(Bad | Yes) is not considered in this calculation ?

@MindScape322 3 жыл бұрын

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 KZfaq video for Bag of Words and stopwords removal.

@tanvishinde805 3 жыл бұрын

@@MindScape322 oh yes! Thank you

@MercyGraceThomas 4 жыл бұрын

Gibbs algorithm?