Tutorial 37: Entropy In Decision Tree Intuition

This is one of the best explanation thankyou somuch sir

thanku a lot🙏😊

What do you mean by feature?

@krishNaik, I like your videos very much as they are quick reference guides for me to quickly understand something required for interview prep or for any project.

Just noticed here that, you mentioned Entropy is a measure of purity. But, it is a measure of impurity which makes more sense. The more the value of entropy, more is heterogeneity in the variable.

You don't explain the intuition though.

Dear Krish Naik Sir.
Could you please recheck the calculation. As per my calculation entropy for f2 node where the split is 3|2 is 0.97 and not 0.78 ?
Kindly correct me if I am wrong.

Hi, there might be calculation mistake in the entropy part. its not 0.78. Can you please mention that in a caption in the video or a description. So that people dont mistaken it in the future. Great video!!

tidak membantu

Awesome video.

why this entropy in bits? as for normal its about 0.97, and how can i convert my entropy iinto bits

i have one question .at root node is the gini are Entropy is high are low..

very well understandable your teaching curriculum.

thank you

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Can we use same feature for multi level split in the decision tree?

thank you. we all need teachers like you. god bless you. you're a blessing for us college students who are struggling with offline colleges after the reopening.

Hi Krish, can you please explain the process of calculating probability of a class in a decision tree and whether we can arrive at the probability from feature importance

good explanation

Krish, I love you so much, more than my girlfriend, zillions like from my side. You always make knotty problems so simple

Thank you, Krish sir.

how is 0.79 bits when you compute it? someone pls explain

Thanks Krish

Explained in a great way ...Thank you krish

Entropy is thermodynamics concept measure tha energy, why using mechine learning.

Nice explanation. But actuallly we dont use this formula while modelling. We just set the parameter of decision tree to either entropy or gini. So when does this formula of entropy really help??

Sir, here u didn't mentioned that how f3 is in right side and how f2 is in left side node. As u said the attribute having less entropy is selected for split. This is understood but why f2 is on left and f3 os on right?

Can you mathematically explain how you obtained entropy=1 for a completely impure split(yes=3, no=3)?

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In the formula of Entropy what is the significance of log base 2, why not simple log having base 10?

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You clearly explain the mathematics of machine learning algorithms! Thank you for your effort.

Great introduction to the topic, thank you

excellent explanation man, thanks

Hi Krish,
Have you explained how decision tree works? because im not finding it

Nice explanation. Cheers =]

Thank you for a great tutorial. The entropy value is actually 0.97 and not 0.78.

Thank you, this was very helpful!

Super Awsome!

very much helpful sir thank you you are best :)

if we have very high dimensional data , how do we apply decision tree ?

One of the great teacher in the Machine Learning field. You are my best teacher in ML.Thank you so much sir for spreading your knowledge.

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in my opinion, calculating entropy is sufficient and we don't require information gain, as in information gain we simply subtract from the entropy of attribute from the entropy of dataset; the entropy of dataset is always constant for a particular dataset.

As Entropy of pure node is zero..I think Entropy is measure of impurity..lesser the Entropy..more pure the node is

please upload the video for regression tree also and discuss it in detail manner

Yours videos are very nice, but you really need to improve the quality of your microphone