3 Decision Tree | ID3 Algorithm | Solved Numerical Example by Mahesh Huddar

3 Decision Tree - ID3 Algorithm Solved Numerical Example by Mahesh Huddar
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[@techfort]

Dear Mahesh sir,
If I'm not wrong then At 10:30 the Entropy (S) must be 0.7219 .
And Gain(S,A3) answer would be 0.7219-0-0=0.7219.
Thank you

[@smita_r]

Thanks for clearing the dought here

[@pratik2617]

@@smita_r doubt hota ha🙂🙂

[@ohiogozaimasu]

Haa, entropy of parent sir ne 0.7219 calculate kri h, bs misprint ho gya h last step m

[@padelis_doulis]

you have a mistake at Gain(s,a1) at 3:50, it should be (-5/10) * 0 and not (05/10)*1 since E(Sfalse)=0.0(result is right tho, you just misspelled). Other than that, nicely explained, learnt how this works thanks to your vids.

[@vishalrana6822]

yes

[@VamshiChaithanya]

At 9:46, Entropy(S) should be 0.7219 right ? SO the answer must be 0.7219-0.6486=0.07294 . Kindly someone correct me if wrong

[@srikanthbalakrishna4042]

yup,you're right

[@harrouzmouad3726]

you'are right buddy

[@MOHAMED52904]

You're right , I got the same result as yours.

[@neonargon8048]

Yeah right

[@sikindarshaik9472]

Yes you right

[@ramyasrikanteswara5366]

Entropy values of false in a1 is zero but while calculating final value you have taken it as 1

[@ayushmanbhargabagopalbiswas]

Love you sir, it's help in our semester exam❤

[@MaheshHuddar]

Welcome
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[@manishasamal9474]

Very nicely explained 👍👍

[@emmanuellafakonyui9550]

Thank you so much for this video. I really do appreciate. It's amazing how explicit you made it and very understandable too. I was able to finish my assignment in a short interval (zero stress)

[@MaheshHuddar]

Welcome
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[@emmanuellafakonyui9550]

@@MaheshHuddar Of course!

[@quangminhtran8562]

Tks sir, It''s help me so much in my semester exam

[@MaheshHuddar]

Welcome
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[@kushalsharma2018]

The guys confused on how to put log base 2 in the calc. In most calcs u can not directly put log base 2 but instead u can find it out by for example: Find log base 2 of 5, it can be done by: (log 5 )/ (log2) where the log is in base 10

[@evo-star7850]

This actually quite a good tip.

[@darketernal5792]

damn!! you saved me man 👊

[@johnrooney9972]

is it okay to leave a2 out of the tree?

[@self.__osman]

God bless you sir

[@MaheshHuddar]

Thank You
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[@junaidiqbal5018]

nicely explained pal

[@leonardkiptala9734]

Thank you so much.

[@MaheshHuddar]

Welcome
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[@lenaleo3322]

thankyou for sharing this vdo

[@MaheshHuddar]

Most welcome 😊
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[@dakshitkatakam]

how to calculate entrophy value

[@yourbrother9941]

If there is equal gain, which one we have to consider for next node??

[@santoshvadisala5324]

Correct me if I am wrong, but I wonder where is the numerical data in this dataset? every feature is categorical rt ?

[@user-sz2ue6sv2h]

please make video on C4.5

[@hawkitnow8135]

Thnx

[@MaheshHuddar]

Welcome
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[@miklospoth1950]

Instances 1 and 2 are the same, also 4 and 5 are the same. Shouldn't we merge them into one single instance?

[@thegeethaart]

Sir plz can you solve even last year dec2019/jan2020 paper problems

[@minalpatil5671]

Is it ok to leave out a2?

[@smithtuscano4576]

What is log2

[@vinayg2133]

Sir can u just say wic entropy value must be consider after dividing table in above example should we consider entropy(true) r intial entropy

[@padelis_doulis]

after you devide table, because you only care about the left part of the tree (True), since the right part doesnt need any more questions asked, you consider the Entropy(true) to check for 1 2 6 7 8. So your S there, should be the same as Entropy(True). and then you check a2 and a3 as the video shows.

[@alimuddinkhan25]

finally a1 -sir you are put false=1 this wrong
please sir correction false = 0

[@sravanr2275]

Today I got this question only in the exam but I seen yesterday and Today only that 23 minutes video that's it 😕🫤🙁☹️😪😭😪🙂🙃🥲🫠🥹🫥😑😐😬😶😶‍🌫️🤐🥺😔🫡😞😓😟😥😢😒🙄😮‍💨😤

[@hawkitnow8135]

Thnx

[@MaheshHuddar]

Welcome
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[@hawkitnow8135]

@@MaheshHuddar i want some examples on perceptron and margins , cant find it , would u help me plz