Decision Trees Geometric Intuition | Entropy | Gini impurity | Information Gain

Decision Trees use metrics like Entropy and Gini Impurity to make split decisions. Entropy measures the disorder or randomness in a dataset, while Gini Impurity quantifies the probability of misclassifying a randomly chosen element. Information Gain, derived from these metrics, guides the tree in selecting the most informative features for optimal data splits, contributing to effective decision-making in classification tasks.
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⌚Time Stamps⌚
00:00 - Intro
00:14 - Example 1
03:00 - Where is the Tree?
04:00 - Example 2
06:09 - What if we have numerical data?
07:57 - Geometric Intuition
10:50 - Pseudo Code
11:54 - Conclusion
14:00 - Terminology
14:53 - Unanswered Questions
16:16 - Advantages and Disadvantages
18:04 - CART
18:45 - Game Example
21:45 - How do decision trees work? / Entropy
22:15 - What is Entropy
25:40 - How to calculate Entropy
29:40 - Observations
31:35 - Entropy vs Probability
36:20 - Information Gain
41:40 - Gini Impurity
50:30 - Handling Numerical Data

[@vatsalyaa.m]

I had to like when you said you dont know BTS... Respect

[@shivamshaw_07]

lol

[@prasadbhandarkar7757]

Please start deep learning tutorial series as well . Your explanation makes each and everything clear . Thank you so much for the one of the best tutorial series on machine learning ❤️

[@arpitchampuriya9535]

Decision Tree categorical on variables- 00:28
Decision tree on numerical variables - 06:08
Geometric Intuition - 07:57
How decision tree works? - 10:49
Terminology- 13:56
Common doubts regarding Decision tree - 14:51
Advantages and Disadvantages of decision tree - 16:12
Interesting game to understand decision tree - 18:30
Entropy - 21:42
Entropy calculation - 25:28
Entropy vs Probability graph - 31:30
Entropy for continuous variables - 33:16
Information gain - 36:12
GINI Impurity - 41:40
Why to use GINI over Entropy? - 48:36
Handling numerical data - 50:28

[@aienthu2071]

Your Channel is a Gold Mine 💎🔥🔥

[@kislaykrishna8918]

Great teacher you are . Crystal clear understanding 🙏

[@kanakkanak1520]

Very clear explanation as always. Thanks!

[@HimanshuSharma-we5li]

You are a brilliant teacher sir .

[@BCS_NOORJABEEN]

thank u so much sir, this is the best video i have seen on decision tree.

[@ty_25_tanmayijadhav29]

Explanation is excellent sir.. thank you

[@user-ll1fc1rd5t]

simply awesome explanation... it was very helpful. Thanks

[@devyaniwaje2758]

great explanation on every concept

[@prateeksinha08]

Great Lecture sir, whatever topics you taught entropy, Information Gain, gini impurity, I don't think anyone else could teach with this much easy.
Hats off to you sir

[@abanticaadhikary6941]

Thank you for making it so easy and simple

[@thatsfantastic313]

♥♥ No words Sir no words

[@ParthivShah]

Thank You Sir.

[@ujefmalek77]

Thank you ❤️

[@gaurikarale7248]

Thank you so much 🥰

[@karanparashar6824]

Very informative video.

[@krunalgandhare4026]

I guess it should be -4/4log(4/4) - 0/4log(0/4) for the middle node at 39:12

[@vishnujatav6329]

Little bit understand the decision trees. Thank you

[@statisticsguruji856]

thank you sir 🙂

[@chessfreak8813]

I think linear reg assumptions, ROC AUC MPAE are remaining so could you plz make videos on that? Because I observed u do reaserch and then make videos.. Because when I read some blogs on medium or towardsdatascience I can relate ur explanations.. Thanks!!

[@monikachivate6287]

Thank you sir

[@abhishekagarwal785]

Thanks for awesome video , really liked it..! At 40:31 should it be 0.94-0.69?

[@shekharbanerjee9738]

Does not know BTS, best teacher ever

[@finestopedia5352]

Just to make things clear lim x tends to 0 x log(x) =0 Hence -0/5log(0/5) is 0. We cannot put x=0 as x=0 is not defined

[@mohammadvahidansari8212]

Great work sir!!!!!
your explanation is too good.....
Will you upload this type of videos of topic SVM later in future????

[@campusx-official]

Yes all the algorithms will covered one by one

[@sonalkudva1839]

@CampusX could you please tell me where can i find the link for the paper which explains the difference between GI and entropy

[@GauravKumarGupta-fn8pw]

what to do if we have more then one attributes?

[@rajat9302]

sir there would be mistake, on calculating information gain u consider E(parent) = 0.97 but E(parent) = 0.94 I guess. Please check it once.

[@battelbots_shots5896]

sir example 2 ma sunny and humidity pe dependent ha and rain and wind pe ha model ne ulta bata deya shayad

[@ambrosiasociety]

what if we have more than 1 column then what will we do?

[@AmarSharma60436]

I wish akinator would detect you someday 😢 😁

[@88oitlgftk]

Sound quality 😮

[@mohammadzahid3431]

at time 28.28 (The second table entropy should be .21713 I think)

[@MuhammadNaeem-cz6ub]

max entropy for n class problem is logn to the base 2.

[@prathameshchaudhari4937]

I would like to support this channel with money but the link you provide has 500 rs of minimum payment. Can you please provide with alternate method?

[@saurabhnitkian]

At time 7:50, if the first decision for PL< 2.0 is false, then PL should be greater than 2.0, making the second decision "PL< 1.5 " as wrong.

[@fullthrottlevishal]

entropy of while calculating information gain is 0.94 not 0.97sir

[@Julaiarvind]

sir, please make a video on Linear Regression Assumptions

[@AbdulRahman-zp5bp]

sir please share those slides
thanks :)

[@Vernika19]

Hi CampusX, although the explanation is great but I would advice to use word certinity or order in place of knowledge because the more certain we are about a data, less entropy is there.

[@kiranravish4700]

can i get vedios notes?

[@ranirathore4176]

20:14 BTS army be like : gazab bezati hai 😂 .

[@univer_se1306]

didnot understood last 8 minutes of video

[@waghakshay447]

Sir unknowingly roast BTS 😂😂 "I don't know BTS"

[@ali75988]

A better approach would have been one single example of how it works - like example hai, per just not like he teaches kae kia kidher sae ho rha with notepad. Secondly, too much dry content in one lecture.
feel free to disagree. I ain't hitler

[@madhusmitamajhi9351]

BTS is a k-pop group and they are world wide famous. Just letting you know

[@pavanyadavalli6888]

i was expecting this kind rply just after sir said idk bts 😂😂