Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka

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[@vigneshviki4955]

code explanation should be slow as it is a key area just moving ver fast

[@mustafabohra2070]

Best tutorial on KZbin!

[@syammangineni5857]

Excellent teaching, great explanation. Thank you sir.

[@YemiAdelaiye]

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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[@monuvishwakarma8133]

How did you decided the position of windy and humidity?

[@STTPwithRajani]

Crystal clear 🙂 thanks 🙏

[@edurekaIN]

You are welcome 😊 Glad it was helpful!!

[@PritishMishra]

Good teaching and animation......

[@gidinated]

thank you for the clear explanation!

[@edurekaIN]

You are welcome :) Glad it was helpful!

[@sarangabbasi2560]

WOW great work

[@vithikapungliya6043]

Best video!

[@omkarparab8780]

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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[@jainharshal3088]

very well explained the math behind the decision tree. thank you

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[@hafzatinnurlatifa2589]

thank you, it's really helpful

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[@mahfuzulhaquenayeem8561]

Thank you a lot for creating this for a beginner like me.

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You're welcome 😊 Glad you liked it!! Keep learning with us

[@codeindia4730]

Kindly . Correct The thing On timestamp 28:42 The last Entropy calculation should be of rainy but taken of sunny again.

[@YemiAdelaiye]

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.

[@Sinhajii06]

Satisfactory explanation among all resources.... 10 out of 10

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[@hammadkhan7927]

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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[@quamar0313]

Thanks for great explanation

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[@pratheekhebbar2677]

what an explanation.simply superb sir .so simple and easily you explained

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[@ML-uy8bs]

its really awsm........ very helpful...

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[@skywalker66ful]

Very Nicely Explained .. Thanks ..

[@jaber_courses]

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

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Hey Jaber! Thank you for appreciating our efforts. You can check out our Complete Machine learning tutorial here: kzbin.info/www/bejne/mGPUZoJ8qd2gbKM Hope this is helpful. Cheers!

[@priyanshvatsal9791]

Awesome explanation

[@kainatanjum7991]

Thank you for making it clear and concise, additionally can you please provide the source code ?

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[@imresuveges8798]

Great explanation! Thanks a lot!

[@manivarmaindukuri7801]

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 .

[@edurekaIN]

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!

[@sankettakle9980]

hey great explanation .. covered all the topics neccesaary could you please share the code

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Thank you. Please share your email id with us (it will not be published). We will forward the code to your email address.

[@asmaalaila9337]

Thx .it's great

[@atharvav]

Right, Alright!

[@silasvick]

Am building a model to predict a likelihood that someone may commit suicide.. Can i use this algorithm?

[@edurekaIN]

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.

[@Raj64395]

Excellent explanation

[@sarojbodepudi4941]

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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Please share your email id with us (it will not be published). We will forward the code to your email address.

[@abhishekjani8695]

CART Algorithm uses Gini Index but you have implemented the dataset using entropy and Information gain so it will not be ID3 Algorithm?

[@nchsrimannarayanaiyengar8003]

wonderful

[@rimpinag6346]

Really great explanation ... awsome video ... i understand very clearly and like it🥰

[@shareInfo2023]

very nice explanation sir .........Great Thanks to You...

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Hey Ram, glad you loved the video. Do subscribe and hit the bell icon to never miss an update from us in the future. Cheers!

[@zuoweizhu1797]

Nice tutorial， Decision Tree well explained

[@sumahullur5138]

great video! You made it simple and clear, thank you so much

[@komalrathod7746]

Thank you for the explanation. Can you please share the code?

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[@raviartgallery6466]

Good explanation

[@EngineersLife-Vlog]

Anyone with no prerequisites will able to understand Edureka all classes.

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[@adhithyarajan2311]

What is decision tree with relearning of nodes?

[@UttamKumar-sc1gw]

Great Video . Thanks much.

[@mahimsd7645]

Nice one ....good

[@talharasheed5322]

Very helpful, really obliged of edureka 🙏

[@surender6320]

Hi, This is Surender, great explanation, can you please provide the python code.

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Thank you. Please share your email id with us (it will not be published). We will forward the code to your email address.

[@rishird5635]

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

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[@aritrasaha1091]

Excellent session . Is it possible to provide the python codes ?

[@anthonyfernandez894]

Absolutely stunning, very well explained, clear ideas, awesome stuff & remarkable cases...substantial learning at high level. Thanks & regards from Costa Rica.

[@santoshbirje2705]

very clear. Thank you so much :)

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[@WasimAkram-vq5if]

Great session

[@qizhang5885]

Very clear explain. May I have the source code?

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[@salehaali1185]

Thanks alot for the wonderful video. kindly share the code plz asap.

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[@jackyhuang6034]

Awesome video. May I have the code that was used at the end of the tutorial? Thanks in advanced.

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[@RajChauhan-hd9hu]

If the training_data is huge then how can we make the necessary changes and get the same correct output?

[@edurekaIN]

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

[@vipulsharma8865]

All Right!

[@vaishnavipraveena7170]

Hi Sir, Please share the link to the code that you have explained above. Thanks.

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Please share your email id with us (it will not be published). We will forward the source code to your email address.

[@Zeus-dc5oh]

Thank you, you made it clear. Could you please share the code?

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Hi, kindly mention your email id in the comments to help us assist you with the required source codes, cheers :)

[@indrajitmukherjee5964]

Awesome video guys. one small request can I get the demo code used here for my practice ?

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[@jayedakbar2425]

Thanks for the great video. Can i have the code please?

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[@simhanarasimha386]

Nice explanation. Could you please share the code, it would be helpful, Many thanks in Advance!

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Thank you. Please share your email id with us (it will not be published). We will forward the code to your email address.

[@এসোশিখিসহজে]

great explanation..may i have the code

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Thanks Obaid. Please share your email address, we will send you the code.

[@suhasnayak4704]

What are computational complexity for Decision Tree? Can you please give analysis for tree training phase and prediction phase please.

[@rudrajadageethakrishna3171]

Sir please can you keep python live class pls sir

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[@vishwanathravula9239]

Hi, Could you guys give me some guidance to implement the decision tree algorithm in torch7 using Lua.

[@edurekaIN]

Hey Vishwanath, "indico.cern.ch/event/472305/contributions/1982360/attachments/1224979/1792797/ESIPAP_MVA160208-BDT.pdf This might be useful to you. Cheers!"

[@mahadihasan-yi5pz]

great help for me. could I get the code?

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[@keerthikeethika9551]

hii sir...nice video please share the source code and dataset

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[@jaikishangupta1824]

Can anyone tell how windy child decided to strong and or weak and how its value is true and false also decided?

[@karthikarg]

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?

[@edurekaIN]

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.

[@sarangabbasi2560]

Can u provide practice problems with solution.

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[@SnehaKondapalli]

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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[@datasciencecamp]

The code and data is not available in description. Please share the link.

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Please share your email id with us (it will not be published). We will forward the code and dataset to your email address.

[@ashwatsharma7511]

Very Well explained ..👍 Can I get code file??

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Hi Ashwat, kindly drop in your email id to help us assist you with the required source codes. Cheers :)

[@sundayagu2078]

Excellent analysis. Thank you and remain blessed.

[@saraaitakil4801]

thank you

[@vivanjain1746]

Great explanation. Can you share the code

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[@huhwhat8831]

Great video. But could you please provide the source code. It will be helpful for us to study it.

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[@hafizibtisam]

sir can i get code of it?

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[@Amitkumar-em4fm]

Nice Video and great explanation .Can i get the source code pls

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Thank you. Please mention your email id (it will not be published). We will forward the code to your email address.

[@maldito21st]

Very informative video. Can you share the code as our reference? Thanks.

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Thank you. Please share your email id with us (it will not be published). We will forward the source code to your email address.

[@pragatisrivastava4872]

Nice video, may i have the code that was used at the end of the video.

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Thank you. Please share your email id with us (it will not be published). We will forward the source code to your email address.

[@poojabhakuni464]

Can you please provide the code, it will be a great help!

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Please share your email id with us (it will not be published). We will forward the code to your email address.

[@davidcecil1477]

Can I please get the entire source code to review please? It'd be of great help!

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[@kritinworld]

Well explained. Can you please share the source code?

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[@shagunsrivastava5837]

hi!! could you please share the link to python code explained in the video??

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Please share your email id with us (it will not be published). We will forward the code to your email address.

[@terryliu3635]

A great refresh of decision tree. Thanks!

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[@omkarparab8780]

Can I get the code ? I couldnt find in the description

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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 : )

[@habibasani8106]

Nice video. I was really helpful. Please can you send me the source code t practice

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Hi Habiba! Can you please share your email id with us (it will not be published). We will forward you the source code to your email address.

[@nahidchahin5716]

Nice video. I was really helpful. Please can you send me the source code??

[@Radhekrishnashivay]

Hello, Thanks a lot for this tutorial...can I get the code please? as I'm facing few errors while running it like I Got the error that Question takes no argument. Please share the code.

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Hi Parul, we do provide practice codes to enhance your learning experience, kindly drop in your email id to help us assist you with it. Cheers :)

[@abhishekghaskata9130]

How we know that which algorithm is best for our data????

[@edurekaIN]

Hi Abhishek, in practice, some form of cross-validation is typically applied. However, there are ways to make an informed pre-selection. You can go with a maximum margin classifier such as support vector machines. It can be considered the best off the shelf classifier to date.

[@peacetym]

Is the range of entropy 0 to 1 as shown at 23:28 ?

[@edurekaIN]

Hey Sai! Yes, entropy has a value between 0 and 1.

[@nuraisyah9509]

what did you import for doing tree decision?

[@edurekaIN]

Hi Nur, You have to first import the required libraries and datasets. Hope this helps.

[@hariharancse7273]

How to use decision tree cart algorithm in various problems sir! I am develop the project of"projector control" in supervised programming can I create??

[@edurekaIN]

Hey! Yes, You definitely can create.

[@mansigandhi7821]

I am not able to find the code i.e. jupyter notebook which sis mentioned in the course

[@edurekaIN]

Please share your email id with us (it will not be published). We will forward the code to your email address.

[@AshishPatel-kn3kc]

where can we find pyhton code ?

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Please share your email id with us (it will not be published). We will forward the code to your email address.

[@supriyakarmakar1111]

Pls tell that how to print the decision tree .....

[@edurekaIN]

Hey Supriya, Yes you can. You will have to use sklearn and work though it in iterations. It is pretty simple. Happy learning!

[@harishkumargajjela]

Please share the last cheat sheet scikit learn

[@edurekaIN]

Hi, please refer to the following link: www.edureka.co/blog/cheatsheets/python-scikit-learn-cheat-sheet/