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I HAVE A QUESTION: Why was the temperature feature totally ignored in the dataset while building the decision tree? While Outlook, Humidity and Windy were all chosen.
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We are super happy that Edureka is helping you learn better. Your support means a lot to us and it motivated us to create even better learning content and courses experience for you . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
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Hi there, this probably depends on the kind of data you have collected. If you have good labelled datasets which can help you understand the persons mental state then this algorithm would definitely work. But if it is something where your dataset is broken and not complete and you cannot predict any reason which causes the suicide, then unsupervised algorithms will work. Hope that is helpful.
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Well explained !! got a high level overview , for intuitive understanding of few terms referred google . All in all thankyou for this vid just one correction at 30:15 one entropy should of rainy but both are written as sunny.
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very informative video I really need to learn more about Machine Learning in Python I wish that you post more videos in that useful and interesting topic. Like d this video , Thank you
Hey Jaber! Thank you for appreciating our efforts. You can check out our Complete Machine learning tutorial here: ruclips.net/video/b2q5OFtxm6A/видео.html Hope this is helpful. Cheers!
Sir , I was assigned a project on fraud detection . Which algorithms should I learn to train many transactions and detect if a transaction is legitimate or fraud ? I wanted to implement in Python . Please guide me in this project .
Hey Manivarma, Classification and clustering algorithms are good for fraud detection and anomoly detection. So algorithms like, SVM, KNN, K-Means, Decison trees, Random forest are relevant. Hope this helps!
The code is not there in the description .. can you get me that? The video lecture was awesome.. I tried doing alongside..but have some errors. If given the sample code may be I can check it out. Cheers
Hey Rishi, thank you for watching our video. We are glad that you liked our content. Sure, mention your email address and we will share it with you. Cheers :)
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Absolutely stunning, very well explained, clear ideas, awesome stuff & remarkable cases...substantial learning at high level. Thanks & regards from Costa Rica.
This lecture was exactly hitting on the nail, this helped in clarifying my doubts. Is it possible to share the link for this python code, that could be very helpful! Thanks
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Hey Raj, You can set the variable to r rangle. For example, if you have around 5000 tuples, then you can use just 200 tuples and then assign it to train data. After that you can use the algorithm and test the data based on the chosen tuples
Hey Vishwanath, "indico.cern.ch/event/472305/contributions/1982360/attachments/1224979/1792797/ESIPAP_MVA160208-BDT.pdf This might be useful to you. Cheers!"
Thanks for the appreciation, Keerthi! Please mention your email id (it will not be published). We will forward the code and dataset to your email address.
Why didn't 'temperature' come into picture while constructing the decision tree? Is it because the information gain is least compared to all other nodes? if yes, then every time when we construct the decision tree should we ignore the parameter with least info gain?
Hey, great video but I have a question i am working with a data set that has 569 instances and 30 variables, problem is that the variables aren't like the example, they are not standard options, like shown in the video where outlook has 3 distinct options, these variables are all doubles, they range from 7.33 up to 22.45 or something like that, so i'm really not sure how to calculate entropy for that
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Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
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Hey Sweta, "Formula for entropy is -P(YES)xlog2(P(YES)-P(NO)xlog2(P(NO) Now since out of 14 instances, we have 9 Yes and 5 No. So put 9 and 5 in the formula above you will the entropy as 0.94. For a better explanation please jump to 26.12 minutes of this video." Hope this helps!
Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Python Machine Learning Course curriculum, Visit our Website: bit.ly/2OpzQWw
code explanation should be slow as it is a key area just moving ver fast
Best tutorial on RUclips!
How did you decided the position of windy and humidity?
Excellent teaching, great explanation.
Thank you sir.
Thank you! I have been looking for a video all week that would break "decision tree" down for me. This is it!
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Kindly . Correct The thing
On timestamp 28:42
The last Entropy calculation should be of rainy but taken of sunny again.
I HAVE A QUESTION: Why was the temperature feature totally ignored in the dataset while building the decision tree? While Outlook, Humidity and Windy were all chosen.
thank you for the clear explanation!
You are welcome :) Glad it was helpful!
Crystal clear 🙂 thanks 🙏
You are welcome 😊 Glad it was helpful!!
very well explained the math behind the decision tree. thank you
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I think it is best video for decision tree. Can you please give me the notes that you used to teach.
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Thanks for great explanation
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thank you, it's really helpful
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Thank you a lot for creating this for a beginner like me.
You're welcome 😊 Glad you liked it!! Keep learning with us
WOW great work
Best video!
Satisfactory explanation among all resources.... 10 out of 10
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Good teaching and animation......
its really awsm........
very helpful...
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what an explanation.simply superb sir .so simple and easily you explained
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Am building a model to predict a likelihood that someone may commit suicide..
Can i use this algorithm?
Hi there, this probably depends on the kind of data you have collected. If you have good labelled datasets which can help you understand the persons mental state then this algorithm would definitely work. But if it is something where your dataset is broken and not complete and you cannot predict any reason which causes the suicide, then unsupervised algorithms will work. Hope that is helpful.
Very Nicely Explained .. Thanks ..
Right, Alright!
Thank you for making it clear and concise, additionally can you please provide the source code ?
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Well explained !! got a high level overview , for intuitive understanding of few terms referred google . All in all thankyou for this vid just one correction at 30:15 one entropy should of rainy but both are written as sunny.
Hey:) Thank you so much for your sweet words :) Really means a lot ! Glad to know that our content/courses is making you learn better :) Our team is striving hard to give the best content. Keep learning with us -Team Edureka :) Don't forget to like the video and share it with maximum people:) Do subscribe the channel:)
Thank you for the explanation. Can you please share the code?
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very informative video I really need to learn more about Machine Learning in Python I wish that you post more videos in that useful and interesting topic. Like d this video , Thank you
Hey Jaber! Thank you for appreciating our efforts. You can check out our Complete Machine learning tutorial here: ruclips.net/video/b2q5OFtxm6A/видео.html Hope this is helpful. Cheers!
Sir , I was assigned a project on fraud detection . Which algorithms should I learn to train many transactions and detect if a transaction is legitimate or fraud ?
I wanted to implement in Python .
Please guide me in this project .
Hey Manivarma, Classification and clustering algorithms are good for fraud detection and anomoly detection. So algorithms like, SVM, KNN, K-Means, Decison trees, Random forest are relevant.
Hope this helps!
hey great explanation .. covered all the topics neccesaary could you please share the code
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CART Algorithm uses Gini Index but you have implemented the dataset using entropy and Information gain so it will not be ID3 Algorithm?
Awesome explanation
Great explanation! Thanks a lot!
Really great explanation ... awsome video ... i understand very clearly and like it🥰
The code is not there in the description .. can you get me that?
The video lecture was awesome..
I tried doing alongside..but have some errors. If given the sample code may be I can check it out.
Cheers
Hey Rishi, thank you for watching our video. We are glad that you liked our content. Sure, mention your email address and we will share it with you. Cheers :)
Very clear explain. May I have the source code?
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Awesome video guys. one small request can I get the demo code used here for my practice ?
Hey Indrajit! We are glad you loved the video. Please do mention your email ID over here and we will send the files to you. Cheers!
Thx .it's great
Absolutely stunning, very well explained, clear ideas, awesome stuff & remarkable cases...substantial learning at high level. Thanks & regards from Costa Rica.
Anyone with no prerequisites will able to understand Edureka all classes.
Hey Souman, that is our aim. Thank you for appreciating our efforts! Keep supporting us, cheers :)
Excellent session . Is it possible to provide the python codes ?
great video! You made it simple and clear, thank you so much
This lecture was exactly hitting on the nail, this helped in clarifying my doubts. Is it possible to share the link for this python code, that could be very helpful! Thanks
Please share your email id with us (it will not be published). We will forward the code to your email address.
Very helpful, really obliged of edureka 🙏
Great Video . Thanks much.
very nice explanation sir .........Great Thanks to You...
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wonderful
Nice tutorial， Decision Tree well explained
What is decision tree with relearning of nodes?
Excellent explanation
Hi, This is Surender, great explanation, can you please provide the python code.
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Hi Sir, Please share the link to the code that you have explained above. Thanks.
Please share your email id with us (it will not be published). We will forward the source code to your email address.
Thanks for the great video. Can i have the code please?
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Nice one ....good
If the training_data is huge then how can we make the necessary changes and get the same correct output?
Hey Raj, You can set the variable to r rangle. For example, if you have around 5000 tuples, then you can use just 200 tuples and then assign it to train data. After that you can use the algorithm and test the data based on the chosen tuples
very clear. Thank you so much :)
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Thanks alot for the wonderful video. kindly share the code plz asap.
Please share your email id with us (it will not be published). We will forward the code to your email address.
Awesome video. May I have the code that was used at the end of the tutorial? Thanks in advanced.
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Good explanation
Thank you, you made it clear. Could you please share the code?
Hi, kindly mention your email id in the comments to help us assist you with the required source codes, cheers :)
great help for me. could I get the code?
Hi Hasan, kindly drop in your email id to help us assist you with the required source codes. Cheers :)
Is the range of entropy 0 to 1 as shown at 23:28 ?
Hey Sai! Yes, entropy has a value between 0 and 1.
Hi, Could you guys give me some guidance to implement the decision tree algorithm in torch7 using Lua.
Hey Vishwanath, "indico.cern.ch/event/472305/contributions/1982360/attachments/1224979/1792797/ESIPAP_MVA160208-BDT.pdf
This might be useful to you. Cheers!"
The code and data is not available in description. Please share the link.
Please share your email id with us (it will not be published). We will forward the code and dataset to your email address.
Great session
great explanation..may i have the code
Thanks Obaid. Please share your email address, we will send you the code.
Very Well explained ..👍
Can I get code file??
Hi Ashwat, kindly drop in your email id to help us assist you with the required source codes. Cheers :)
hii sir...nice video please share the source code and dataset
Thanks for the appreciation, Keerthi! Please mention your email id (it will not be published). We will forward the code and dataset to your email address.
Can u provide practice problems with solution.
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All Right!
Why didn't 'temperature' come into picture while constructing the decision tree? Is it because the information gain is least compared to all other nodes? if yes, then every time when we construct the decision tree should we ignore the parameter with least info gain?
Hey, You are correct, it is because it is the least compared element. Not that you should always ignore it but it depends on the use case.
can u please share the code it would be helpful
Prior thank you
Hey Pratima, hope you found the video informative. Please do share your email id(we won't publish it) so that we can mail the files to you. Cheers!
Hey, great video but I have a question i am working with a data set that has 569 instances and 30 variables, problem is that the variables aren't like the example, they are not standard options, like shown in the video where outlook has 3 distinct options, these variables are all doubles, they range from 7.33 up to 22.45 or something like that, so i'm really not sure how to calculate entropy for that
Hey, Entropy is straightfoward and really simple to calculate. Can you elaborate?
Great explanation. Can you share the code
Thank you. Please share your email id with us (it will not be published). We will forward the code to your email address.
Nice explanation. Could you please share the code, it would be helpful, Many thanks in Advance!
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Sir please can you keep python live class pls sir
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hi could you please share the ppt material and code used in this video. This is very helpful for my assignment
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sir can i get code of it?
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Excellent analysis. Thank you and remain blessed.
What are computational complexity for Decision Tree? Can you please give analysis for tree training phase and prediction phase please.
Can I get the code ? I couldnt find in the description
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Can you please provide the code, it will be a great help!
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hi!! could you please share the link to python code explained in the video??
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Nice Video and great explanation .Can i get the source code pls
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Nice video, may i have the code that was used at the end of the video.
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Very informative video.
Can you share the code as our reference?
Thanks.
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A great refresh of decision tree. Thanks!
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where can we find pyhton code ?
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Can anyone tell how windy child decided to strong and or weak and how its value is true and false also decided?
Well explained. Can you please share the source code?
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where is code in description
Hey Digi Bro, mention your email address and we will send it over. Cheers :)
what did you import for doing tree decision?
Hi Nur, You have to first import the required libraries and datasets. Hope this helps.
hi could you please share the code..
I am not able to find the code i.e. jupyter notebook which sis mentioned in the course
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Great video. But could you please provide the source code. It will be helpful for us to study it.
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Can I please get the entire source code to review please? It'd be of great help!
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thank you
Nice video. I was really helpful. Please can you send me the source code t practice
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Nice video. I was really helpful. Please can you send me the source code??
hi i have a question
how did you calculated total entropy as 0.94.p[lz any one help
Hey Sweta, "Formula for entropy is -P(YES)xlog2(P(YES)-P(NO)xlog2(P(NO)
Now since out of 14 instances, we have 9 Yes and 5 No. So put 9 and 5 in the formula above you will the entropy as 0.94. For a better explanation please jump to 26.12 minutes of this video."
Hope this helps!
@@edurekaIN Thanks...I was searching for this Comment