Tutorial 37: Entropy In Decision Tree Intuition
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- เผยแพร่เมื่อ 10 ส.ค. 2019
- Entropy gives measure of impurity in a node. In a decision tree building process, two important decisions are to be made - what is the best split(s) and which is the best variable to split a node.
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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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You are doing an awesome job with our expecting returns. good job Krish, You just nail down the concepts in a line or two thats the way i like it.
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.
You clearly explain the mathematics of machine learning algorithms! Thank you for your effort.
This is what I was looking for. Thank you so much for making this video. Eagerly wait for video on information gain. Please keep going 🙏
You cleared my all doubts about Entropy..... Excellent Explanation 😍😍😍😍
Thank you Thank you Thank youuuuu!! After this I am ready for my test tomorrow.... You are boss with these concepts!!.. Please keep making more. I''ll definitely subscribe and share with friends.
Thank you so much for providing the videos with detail explanations.
Good explanation Krish.Now my misconceptions about decision trees is dwindling away.Thanks
Best channel for Data Science Beginners
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!!
Very nicely explain sir. Thanks a lot. Waiting eagerly for next video on information gain.
Great explanation! Thank you :)
Explained in a great way ...Thank you krish
Great introduction to the topic, thank you
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Nice explanation...... I am learning a lot
Great bro ..thanks for uploading it.
Thank you, this was very helpful!
Good Video, I think you should add gini impurity in the video to explain the decision tree splits, also what is the difference between entropy and gini impurity. Good Video
excellent explanation man, thanks
This is one of the best explanation thankyou somuch sir
very well understandable your teaching curriculum.
very much helpful sir thank you you are best :)
Awesome video.
You should start explaining from the root node.. Like take entropy of all f1, f2 ,f3 first.. then select the best one as the root node, then calculate entropy for remaining data for f2 and f3, and select next best entropy as the node... and continue the same process
bro you look like a great teacher
Thank you, Krish sir.
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
Great yaar!!!
Hi Sir, this video is 37th in ML playlist but we don't have any decision tree video before it.
Nice explanation.... But looking for deep learning video..Please don't stop DL in-between
Thanks Krish
Best explanation
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.
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.
Thank you for a great tutorial. The entropy value is actually 0.97 and not 0.78.
Yes I was thiking the same
He just gave an example but no computed the value
good explanation
I always think it's hard until you convice me how ridiculousely easy it is ..
please upload the video for regression tree also and discuss it in detail manner
@2:16 Entropy is "measure of impurity" thats why we tried to decease the entropy
hi can you pl add link for Gini Index video ? Also pl let me know in which playlist these videos are ? Thanks
Could you please create a video on decision tree random forest and other classification algorithm from very scratch which could be helpful for new learner or newbies in data science
Thank you Sir 👍
Nice explanation. Cheers =]
thanku a lot🙏😊
GOOD ONE
Sir,
To select an attribute at a node in a decision tree we calculate information Gain which ever is having highest that we select as the best attribute at that node but for an example I am getting all the 4 attribute information gain same.
When I browsed in net it is saying that if we have all the attribute information gain as same then we have to select the best attribute according to their alphabetical order for example if we have A,B,C,D
We have to select A first then B,C and D
Is the procedure is correct or any other explanation can u give please
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Super Awsome!
No doubt you have wonderfully explained, What if we have multiple classes in our target variables with not only binary Yes or No? Like a boy, girl and others?
Concept remains same, only the number of choices of split increases. So it is technically more difficult to get the optimal trees using information gain.
waiting for the next video
Sir, May you please make a video clip on the Decision tree?
thank you
Sir Can you also upload about "Information Gain"?
Can we use same feature for multi level split in the decision tree?
Brilliant
Thanks for the video. At 05:48 , how does -3/5log2(3/5)-(2/5log2(2/5)) equal 0.78 ??? I think the correct answer ist 0.971
Could you explain?
you're right i calculate it in python and i found it = 0.9709505944546686
yes you are right
can you tell me how to calculate log of 3/5
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?
I couldn't find any videos for information gain. Could you please upload
As Entropy of pure node is zero..I think Entropy is measure of impurity..lesser the Entropy..more pure the node is
Hi Krish,
Can you please share -Decision tree for Regression?
Having problem in understanding DT incase of regression
i have one question .at root node is the gini are Entropy is high are low..
Hi Krish,
Have you explained how decision tree works? because im not finding it
if we have very high dimensional data , how do we apply decision tree ?
so based on entropy we select the parent node?
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??
Nice Video How to use#Linear_Regression in #Machine_Learning
why this entropy in bits? as for normal its about 0.97, and how can i convert my entropy iinto bits
how to make fuzzy c4.5 on same data-set?
What do you mean by feature?
How did you get 0.78 ?
Can you mathematically explain how you obtained entropy=1 for a completely impure split(yes=3, no=3)?
I think you would have got it by now, this is for those who are looking for the mathematical explanation.
Entropy (3 yes and 3 no)=
= -(3/6) log_2 (3/6) - (3/6) log_2 (3/6)
= -(1/2)(-(1/2)) - (1/2)(-(1/2))
= 1/2 + 1/2
= 1
@@no-nonsense-here log_2(3/6) is -1 not -1/2
how is 0.79 bits when you compute it? someone pls explain
Can you please give the overview of Decision Trees as you have given for Random Forest
Waiting for information gain bro
Hello sir, i have a question like how does decision tree works in mixed type dataset i.e it includes bot categorical and numerical data type. Suppose its a regression problem and data set include both data type so how will algorithm deal with categorical data type in this?
From documentation of sklearn.
When there is no correlation between the outputs, a very simple way to solve this kind of problem is to build n independent models, i.e. one for each output, and then to use those models to independently predict each one of the n outputs. However, because it is likely that the output values related to the same input are themselves correlated, an often better way is to build a single model capable of predicting simultaneously all n outputs. First, it requires lower training time since only a single estimator is built. Second, the generalization accuracy of the resulting estimator may often be increased.
With regard to decision trees, this strategy can readily be used to support multi-output problems. This requires the following changes:
Store n output values in leaves, instead of 1;
Use splitting criteria that compute the average reduction across all n outputs.
....................................
If it is still not clear, ping me, I will expain.
@@sauravmukherjeecom thanks for you answer. But there is no need to do these things as decision tree can handle both types of data..
Did not say how to select the root node?
Best
Waiting for Information Gain video
Yours videos are very nice, but you really need to improve the quality of your microphone
In the formula of Entropy what is the significance of log base 2, why not simple log having base 10?
since its binary split so base 2 is taken.
Krish, I love you so much, more than my girlfriend, zillions like from my side. You always make knotty problems so simple
What if the class attribute has 3 types of tuples...like Low medium and high...??
you will split them to 3 nodes.
@@rohitborra2507I am sorry but this is not correct. The splitting to the nodes depends on features and not on the classes.
If there are multiple classes, the concept remains absolutely the same, but instead of 2 variables in the entropy calculation now you have 3. So, the technical difficulty of understanding the right way to form the tree becomes more difficult.
Entropy is thermodynamics concept measure tha energy, why using mechine learning.
lower entropy, higher information gain
I think your log calculation is wrong. Calculation as shown at 5:54 in video is giving me result of 0.97 bits
@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.
Entropy value is 0.97 not 0.78
yes you are crt the entropy value is 0.98
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Good explaination however always I observed that you will not explain the meaning of the term on which you made the video and always you will explain things in diplomatic way, please use the simple terms to explain the concepts.
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why a lot of talks tho... just show the example case
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