1. Solved Example Naive Bayes Classifier to classify New Instance PlayTennis Example Mahesh Huddar

1. Solved Example Naive Bayes Classifier to classify New Instance PlayTennis Example by Mahesh Huddar
Here there are 14 training examples of the target concept PlayTennis, where each day is described by the attributes Outlook, Temperature, Humidity, and Wind.
Here we use the naive Bayes classifier and the training data from this table to classify the following novel instance: (Outlook=Sunny, Temp=Cool, Humidity=High, Wind= Strong)
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[@MaheshHuddar]

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[@ShivamPanchbhai]

God bless you
you are much better than my college professor

[@jenishshrestha8613]

again some random dude helping me for my finals , op video

[@ritwickghoshal91]

Exactly point to point, Thank you, this video helped me a lot.

[@MaheshHuddar]

Welcome
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[@amoghsinha4305]

Explained better than my university's professor

[@MaheshHuddar]

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[@nadeemhussain4080]

Beta class kiya karo University's professor bhi acha padhate h

[@special2388]

Infact,You weren't attentive in the classes.

[@abeeranaeem7127]

Us bro us

[@Krishna_Singh_2111]

To University jata kyu h yahi padh le

[@ayushtiwari2009]

Thank you so much sir for this wonderful video, my university professors should learn from you how to teach and deliver in a proper way so that the students can connect.

[@MaheshHuddar]

Welcome
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[@iniyaviji2796]

Sir super super am very much satisfied.your explanation is very clear.god bless you sir thank you my teacher

[@MaheshHuddar]

Welcome
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[@malanosi2869]

sir our very much reputed college is making ppt by taking screenshot of ur videos and also making question paper using ur videos.
100% usage of our parents hard earned money by the college

[@SiddharthGargYT]

lol

[@RaushanKumar-iw5dn]

Sahi bole bhai... professor kya pdhate pta nhi... ye video dekha kr smj aa jata hai concept kya hai..

[@gudurilakshmi3886]

Even our professor uses ur vedio in his ppts😂🙏

[@MaheshHuddar]

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[@ziedboughanmi4345]

Shih meksh haddar barsha ama tbarkallah aalik taaref tfasser khir ml moudir mtaa el fac mtaana

[@ramankumar41]

Sir your explanation is very good !!!!

[@MaheshHuddar]

Thank You
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[@aparajitasingh690]

Thank you for the video sir. Great explanation !

[@MaheshHuddar]

Most welcome!
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[@talibdaryabi9434]

Finally, I found something great for a better understanding of Navie theories. Thank you.

[@MaheshHuddar]

Welcome
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[@NitinSharma-qx4ff]

NAIVE* not navie

[@krishnaa5351]

Most complicated teaching, waste fellow

[@jayanthychakrapani3553]

I really found your video very useful for solving my Homework questions sir, but I was really hoping if you can explain the terminologies in this question : "hobby=tennis ^ good-hacker=yes ^ has-publication=yes", Thank you very much!

[@Jash20243]

Sir you are god,god is great.

[@MaheshHuddar]

Thank You
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[@muskansanglikarmss9101]

Explained in very simple way..thanku sir🙂

[@hayafatima9844]

Dil to krta ha degree bi RUclips sa hi lay lon university ka nam ka to thapa lagny jaty hn baki youtube e RUclips sahara ha

[@mychannel-dg4oc]

Preparing for my exam at the last moment with this video's help ,rather than my class notes 😅

[@estherr7931]

my life saver fr

[@MaheshHuddar]

Thank You
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[@theblacktoe]

helped alot. thankyou

[@msai4856]

Another great.....

[@MaheshHuddar]

Thank you
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[@shabeeb4002]

outstanding explanation sir , really appreciated . thanks a lot for giving us such a wonderful explanation . Keep going 👍👍

[@MaheshHuddar]

Thank You
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[@mohammadmahbubtalukder3824]

thanks man, great job

[@MaheshHuddar]

Welcome
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[@VinayGupta-mf1js]

Sir please, it is wrong answer....because ques given solution only sunny cool high strong...

[@bilalillahi6347]

Wdym

[@kim-iz5we]

really helpful！

[@MaheshHuddar]

Thank You
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[@vdarshh]

thank you sooo much sir 🙏

[@MaheshHuddar]

Welcome
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[@anirudhshankarkhedker4400]

(Outlook=Sunny, Temp=Cool, Humidity=High, Wind= Strong) sir is this condition took randomly? or is there an algorithm to take these conditions?

[@chiragjain291]

It will be given in the question (It is like a test set so it tests that for the given set of attribute values what will be the prediction?)

[@aunekhan3794]

Randomly

[@jaideeprathee5951]

This is the question

[@mitheeleshkatyarmal8159]

Top class explanation, Every minute explained in this video is worth hours of explanation.

[@MaheshHuddar]

Thank You
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[@tejasvinnarayan2887]

What if temperature and humidity is real? Like their values are given and there cannot be any logic derived from the dataset?

[@s0lanav]

Thank you sir for your very good explanation, helped me a lot.

[@govindjangid6201]

Thanks sir.. very well EXPLAINED 😊

[@SivaKumar-rv1nn]

Thankyou sir

[@reretofirst7427]

Why we have to do the normalization,

[@ronakmankale9599]

What if the questions says that find it for a particular tuple?

[@Aftabgani50]

Excellent

[@MaheshHuddar]

Thank You
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[@eesabkhan]

Wow. You definitely know what you are doing!

[@seethaselvam7969]

Thank you so much sir🙏

[@hossainalif3909]

Thank you very much sir....
I was so confused with naive biase.....
This video cleared all my confusions......

[@MaheshHuddar]

Welcome, glad it helps you
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[@hlgsagar3792]

Understood clearly sir, Thnk you so much

[@abcd1386]

Sir, If the instances are not given then what We have to do sir

[@vijayalakshmip6963]

Outlook sunny 2/9 for yes then for No also it should be 3/9 rt could you pls explain

[@Adilkhan-lm6ic]

No. We have to divide the total number of no's and yes's in the data . Total yes=9 and total No=5

[@strydomobile]

I like your way to teach, simple and conceptual. I find most people try to sound smart the whole time at the cost of carrying the message over efficiently. Thank you for this video, its clear simple, and to the point.

[@MaheshHuddar]

Welcome
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[@rishabhmishra279]

you have explained in a very simple way and intuitive ! thanks a lot.

[@MaheshHuddar]

You're most welcome
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[@farhana339]

Thank you so much sir.... ❤️It helped us a lot

[@darshan7673]

what is Vnb?

[@mohammedsadiq69]

Bro..Vera level🔥🔥🔥

[@MaheshHuddar]

Thank You
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[@herosoftheworld7]

Explained better than meticulous teachers🙌

[@shreyashachoudhary480]

Amazing

[@MaheshHuddar]

Thank You
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[@wjrasmussen666]

Best video explaining this on RUclips. Love it. I subbed.

[@MaheshHuddar]

Welcome...

[@fashiononyourway]

Keep making such useful videos sir.I understood clearly, thank you so much

[@tharanibaskaran2761]

My question that is what mean by ai and that is value shouldn't mention please ask

[@rusiraliyanage6643]

sir , is this Naive Bayes classifier is as same as logistic regression ?

[@NehaKumari-ge8wm]

No

[@abdulhameedmalik4299]

Best video

[@mehonal21]

Eyvallah

[@MaheshHuddar]

Welcome
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[@Anitha2076]

How to find the VNB( yes)= 0.53 reply me sur

[@theboyfromtheclassics8390]

It's coming 0.0082

[@CSLEEGANGHALLEY]

for me its 0.030

[@thillaianantham1568]

0.64*0.22*0.33*0.33*0.33=0.0053

[@HariniKaran]

Salute sir tqsm 😭💓💓💓💓

[@nishantsharma8996]

thank you sir

[@anmolgoyal6010]

sir we need to calculate vNB(yes) and no at last step this point not clear

[@kvancadguzel205]

Sir,thanks for the amazing video its so clear. Which book do you use for questions&examples?

[@callofdutysuperheroes]

Sir are you serious, how you understood that this is the most accurate way to teach , love you sir ❤

[@MaheshHuddar]

Welcome
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[@karunanithik1584]

Do we have to multiply all the probablites

[@foods-travel]

i get a different accuracy each time i run a classifier on my data ?

[@divyam69]

shutup

[@rutujajambhulkar4709]

Sir please explain how to find 0.053 ans

[@ramyasri7921]

I need python code for this example

[@cc_1enjoyer487]

Thank you Saaar ful suport class rn

[@data_analytics_study]

Very clearly explained sir, thanks

[@sunnyysn5434]

kcpd explaination bro !! luv u

[@Mazzideno]

Well explanatory ❤❤❤
Somewhere in Nigeria

[@MaheshHuddar]

Thank You
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[@SujeetKumar-tl3lq]

On youtube various beggars are throwing ads, share some other medium to be in loop..

[@MaheshHuddar]

..?

[@hamadakram76]

Are u the same guy as Mahesh Dalle?

[@MaheshHuddar]

Who is that..?

[@kamalakannan9283]

The best one

[@MaheshHuddar]

Thank You
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[@kayenatsabir1811]

exactly i had watch more videos navie bayes but cannot understand now u are one that i can understand

[@MaheshHuddar]

Welcome
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[@MohammedAli-p7e9d]

So we normalize the values of V(yes) and V(no) so we add them.
If the result of the summation = 1: Our solution is correct.
Else:
Our solution is false.
Please help me...

[@MaheshHuddar]

Sum of normalised values should be 1

[@MohammedAli-p7e9d]

@@MaheshHuddar Thanks Sir!

[@neggaznabil7570]

Great thanks for your explanation. I have a question: how you can classify a quantitative variables (NUMERICAL values)with NB?

[@MaheshHuddar]

Are you talking about continuous valued attributes..?

[@neggaznabil7570]

@@MaheshHuddar yes about continues variables. I think Gaussian naive Bayes will be applied in this case!!

[@arpitasamanta1883]

can you explain me the calculation how you got .0053 and .0206

[@shaikhuzma786]

We have to multiply the values 2/9×3/9×3/9×3/9×9/14=5.29 approximately equal to .0053

[@sakshivaity9544]

​@@shaikhuzma786 how ?

[@thundersparky2559]

exactlyyyyy brother this is where i got confused tooo and no one has even discussed this in comment section

[@tahminaakter4749]

Nice explanation. It's very easy to understand for beginners.

[@MaheshHuddar]

Thank You
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[@sharaniloganathan9]

Explained very well sir........ U people r the reason to pass students like me who r studying one day before exam... Thank you so much

[@muhammadibrar9053]

Thanks sir.. very well EXPLAINED 😊

[@MaheshHuddar]

Welcome
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[@mvikramaditya8264]

@ 4:37 Getting different answer sir for Vnb(yes) Getting 0.0053 and vbn(no) Getting 0.0205 sir

[@mvikramaditya8264]

After normalizing I am getting correct answer sir Thank you great explanation

[@MaheshHuddar]

Welcome
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[@jaygupta4214]

are u god or god made me found you at the last moment ??
no faltu bakwas kaam ki baat delivered in accurate way

[@MaheshHuddar]

Thank You
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[@HARI5H.S]

Hey did u multiple the values fr yes nd no instance?

[@ReshmaA-wh5us]

Aioo nigalam illana nagalam arrear than tq so muchhhhhhhhhh

[@souravkarjole7587]

is it necessary to cal prob for others too ? cause it was only asked for today = { sunny, cool, high and strong }

[@MaheshHuddar]

Not required to calculate all probabilities

[@DK-il7ql]

how to calculate that Vnb calculations?

[@fatimaiqra2169]

Very exact and to the point explanation...thanks

[@MaheshHuddar]

You're welcome!
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[@govindjangid6201]

Thank you sir 😊

[@MaheshHuddar]

Welcome
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[@beingasif1279]

Condition is given in question or we can make it our self

[@sweetcadbury]

Excellent explanation. Thank you Mahesh

[@MaheshHuddar]

Welcome
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[@user-lp6jv7bf7m]

How to do the algorithm on HTRU 2 data set ?

[@user-lp6jv7bf7m]

I just need an explanation on the data set htru2 iuc set

[@abhish8867]

very greateful to u sir

[@mkjilani]

Thanks!

[@MaheshHuddar]

Thank you for your Support
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[@databuster2431]

Chad 🤝🤝

[@ibraheemzeb5261]

mahesh jaay.. love from NUST jaay...

[@mahanteshg609]

What if overcast was choosen ? That makes Probability 0 , which needs laplacian corrections , sir do you have example with laplacian corrections/smoothing?

[@ottovondrunkencoder]

You need to add an alpha value to prior probabilities to eliminate zero values. Please see this video
ruclips.net/video/O2L2Uv9pdDA/видео.html

[@mehrmalik5860]

how did you decided new instances ? 4:36 time

[@MaheshHuddar]

That was given as part of problem definition

[@islemsahli737]

you are amazing thank you!

[@MaheshHuddar]

Welcome
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[@melaniemurday3138]

Thanks a lot for providing better explanations than my lecturer.

[@MaheshHuddar]

Most welcome!
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[@abdullahmohan9107]

good explanation sir.

[@lekkalaakhila5257]

Good explanation sir