Part 1-Decision Tree Classifier Indepth Intuition In Hindi| Krish Naik

[@krishnaikhindi]

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[@AmeliaMelia-tj3kc]

best teacher

[@netviz8673]

decision tree for both classification regression here classification. 2 techniqiues. ID3 and CART. In Cart the decision tress spilts into binary trees. a)Entropy and Gini Index(Purity spirit) b) Information Gain(feature decision tree split). To check for Pure split two techniques called Entropy and Gini impurity are used and second technique called INformation gain (how the features are selected) is used. When H(S) is zero then that is pure split. And when H(s) is 1 then that is impure split ie equal distribution (eg 3yes and 3nos). The range of entropy remains between 0 to 1. In impure split the Gini impurity comes out to be 0.5 and in pure split it is 0. So the gini impurity ranges between 0 to 0.5. So in impure split the max value of gini impurity is 0.5 and in pure split it is 0. gini impurity is preferrable over entropy because of involvement of log it may slow down Now if you have multiple features, you use information gain to know how to make the tree using the given features whether which feature will start and which one will follow later. The feature starting with which the information gain calculation comes out to be the most should be the one with which the decision tree should be started.

[@techinicalocean8733]

Thank you so much buddy God bless you

[@harshdeepjaggi9715]

wonderful explanation sir.... I'm already enrolled in Data Science with one of the edtech of India... no doubt waha ke teachers bhi accha padhate par jo english mei content hai wo mind mei ek baar mei acche se nhi jaata... ye content hindi wala raise ghus gya mind mei ki bus ab hamesha yaad rhega... Thankyou for your efforts..

[@prathameshekal7308]

मंडळ आभारी आहे

[@jasonbourn29]

Thanx sir in Hindi explanations you tend to cover topics better(English vedios are also of far better quality than anyone else)

[@pritamrajbhar9504]

It is one of the best and simplest explanations till far

[@GopalKrishna-p4m]

after one an era No one will beat you sir !! incredible explanation thankyou so much sir

[@patelanjali2909]

your teaching skill awesomwe.

[@RiteshBhalerao-b2v]

Great explaination...hard to find anywhere else👌👌

[@palakgrover9925]

Amazing Explanation 😃

[@akashlinganwar4810]

hello krish sir... your explanation is easy to understand and anyone can learn easily..thank you sir...😊

[@maukaladka4100]

Worth watching😍😍😍

[@utkarsh5165]

Thank you Krish for Crystal Clear Explanation.❤

[@sandeepbehera3605]

wonderful explanations sir no one can explain like you...🙏🙏🙏 thankyou..sir😇

[@Er_IT_prashantjha]

Bohot achha explain krte ho Sir aap 👌🏻💯

[@aamiransarii]

Many, Many Thanks .....so lovely of you

[@mainakseal5027]

what an amazing tutorial...hats off sirji!!!...

[@sauravsahay8803]

You make everything look so easy

[@adityatiwari7287]

Awesome Explanation....Thanks A Lot....Keep It Up !!!

[@IrfanSaleem541]

Thanks a lot. Love and Respect from Oman

[@DrZubairulIslam]

Thanks Krish, Best Ever Video, Wao.

[@AbhishekSharma-cj9to]

this is really amazing sir 🙏

[@SumitJindal-e3k]

That was awesome

[@Keep_Laughfing]

Sir aise hi video bnate rhiye apko shayd pta bhi nhi hogaa ki ye aapki kitni bdi help h DATA SCIENCE lovers ke liye. Dil se dhanyvaad 🙏🙏🙏

[@maths_impact]

Wonderful explanation given by you sir in hindi.

[@ruhiraj9551]

Hello Krish sir ..thanku so much 🙏 for a very excellent explanation .

[@vidvaanwithromi5213]

wonderfully explained sir!

[@SonuK7895]

Very well explained sirjiiii

[@katw434]

Thanks sir please continue this series

[@ketanitaliya9493]

Thanks it is really helpful and easy to understand

[@osamaosama-vh6vu]

Your legend deae sir thank u be happy 😍

[@jaiprakashsingh756]

sir, I really find your videos very helpful. thanks a lot.

[@gurpreetkaur-pf1bf]

Amazing ❤

[@nisho404]

in one word bosssss

[@dhavalsukhadiya8654]

great explaination sir

[@girishgogate2733]

Thankyou krish sir ........

[@arunchougale5927]

Very Good explained by you .it is lot help me Thank u very much

[@bryan4592]

Amazing video sir

[@mrityunjayupadhyay7332]

Great explanation

[@tuhinbarai7592]

thank you sir..its to understand...

[@AjmalShah-s7n]

Thank you sir G

[@jasanimihir4994]

As always Very well explained. I have one query sir. You told that if the dataset is very big then use gini index otherwise entropy is fine. But finding the entropy is must for the information gain as no mention of Gini index in information gain formula. So is it possible to use gini index to find information gain? Kindly throw light on that. 😊

[@shaiqmahmood]

There is a way to calculate the Information Gain using Gini index as well.

[@abhiWorldIN]

Awesome video

[@BikashKonwar-w7q]

In Entropy formula summation of p(x) * log2(p(x))

[@meenalpande]

Nice explanation

[@anusuyaghosal6961]

sir you said H(s) is the entropy of root node but i think it is the entropy of target attribute

[@krishj8011]

nice tutorial

[@maths_impact]

Wonderful

[@razashaikh8698]

Sir, can we find information gain using ginny impurity?

[@neelkalyani7849]

In calculating information gain, can we use gini impurity instead of entropy?

[@Pankaj_Khanal_Joshi]

Sir sklearrn and seaborn ka video banaiye . thank u

[@pkumar0212]

👌

[@sugandhaarora8174]

is this required for data analyst role?

[@abhishekbhad4029]

Very nice explanation sir.i have one question how to get intership as no one is hiring for fresher

[@VikasSingh-nq5yx]

Sirrrr.... ❤ I have a question 🙋! If interviewer ask a question why we are using minus ( - ) sign in Entropy? Please reply........ ❤

[@saranshsehrawat8544]

its formula

[@neeraj.kumar.1]

Don't worry they don't ask these types of mathematical formulas. They can ask what is Gini impurity.

[@prerakchoksi2379]

wow

[@vatsalshingala3225]

❤❤❤❤❤❤❤❤❤❤

[@rushikeshwaghmare3446]

Sir acc to external sites gini impurity ranges from 0-1

[@rushikeshwaghmare3446]

Please confirm on this…

[@shreyashnarvekar6391]

Where is the practical implementation ? can anyone guide me where can I find it ?

[@priyanshupokhariya8866]

0*log0 is undefined how is it coming 0??

[@aaqibafraaz6215]

Hello sir

[@aaqibafraaz6215]

You r doing a great job

[@SpectreWarfare]

Video volume is very less. It is difficult to listen

[@ShivamSharma-if1oh]

My answer of Entropy is coming 0.6 not 1

[@importantforlifeyasharora9042]

What a definition, entropy ranges 0 to 1 and ginni impurity ranges between 0 to 0.5.😂

[@roshanYadav-y8f]

❤❤❤

[@roshanYadav-y8f]

Kya bhai tum bhi data science ki preparation ker rahe ho