Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka
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- čas přidán 13. 06. 2018
- 🔥 Machine Learning with Python (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") : www.edureka.co/machine-learni...
This Edureka video on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topics covered in this tutorial:
1. What is Classification?
2. Types of Classification
3. Classification Use Case
4. What is Decision Tree?
5. Decision Tree Terminology
6. Visualizing a Decision Tree
7 Writing a Decision Tree Classifier fro Scratch in Python using CART Algorithm
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#decisiontree #decisiontreepython #machinelearningalgorithms
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About the Course
Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.
After completing this Machine Learning Certification Training using Python, you should be able to:
Gain insight into the 'Roles' played by a Machine Learning Engineer
Automate data analysis using python
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and it’s related concepts
Gain expertise to handle business in future, living the present
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Why learn Machine Learning with Python?
Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
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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
edureka! Is there a model for speed-up learning you can share with us in the channel? I would love to understand the process and obtain experience quick. Appreciate your uploads , keep up the good work, you gained 1 more subscriber.
code explanation should be slow as it is a key area just moving ver fast
Excellent teaching, great explanation.
Thank you sir.
Great explanation! Thanks a lot!
great video! You made it simple and clear, thank you so much
How did you decided the position of windy and humidity?
Very helpful, really obliged of edureka 🙏
Really great explanation ... awsome video ... i understand very clearly and like it🥰
Nice tutorial， Decision Tree well explained
Very Nicely Explained .. Thanks ..
Thx .it's great
its really awesome explanation. As usual.
Best tutorial on CZcams!
Absolutely stunning, very well explained, clear ideas, awesome stuff & remarkable cases...substantial learning at high level. Thanks & regards from Costa Rica.
Good teaching and animation......
Great Video . Thanks much.
Thank you! I have been looking for a video all week that would break "decision tree" down for me. This is it!
We are very glad to hear that your a learning well with our contents :) continue to learn with us and don't forget to subscribe our channel so that you don't miss any updates !
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: czcams.com/video/b2q5OFtxm6A/video.html Hope this is helpful. Cheers!
What are computational complexity for Decision Tree? Can you please give analysis for tree training phase and prediction phase please.
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.
very clear. Thank you so much :)
Hey Santosh, we are glad you loved the video. Do subscribe to the channel and hit the bell icon to never miss an update from us in the future. Cheers!
very nice explanation sir .........Great Thanks to You...
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!
Excellent analysis. Thank you and remain blessed.
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:)
Excellent session . Is it possible to provide the python codes ?
Great session
Nice video. I was really helpful. Please can you send me the source code??
Nice one ....good
Excellent explanation
very well explained the math behind the decision tree. thank you
We are very glad to hear that your a learning well with our contents 😊 continue to learn with us and don't forget to subscribe our channel so that you don't miss any updates !
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!"
Satisfactory explanation among all resources.... 10 out of 10
Great to see that our videos and contents are making you perform better and understand better :) We are glad that you've enjoyed your learning experience with us .Thank you for being a part of Edureka's team:) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
what an explanation.simply superb sir .so simple and easily you explained
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
All Right!
WOW great work
thank you
Thank you a lot for creating this for a beginner like me.
You're welcome 😊 Glad you liked it!! Keep learning with us
Right, Alright!
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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thank you for the clear explanation!
You are welcome :) Glad it was helpful!
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.
Awesome explanation
A great refresh of decision tree. Thanks!
Hey Terry, we are glad you loved the video. Do subscribe to the channel and hit the bell icon to never miss an update from us in the future. Cheers!
Great Explanation!! Thank You
Good To know our vedios are helping you learn better :) Stay connected with us and keep learning ! Do subscribe the channel for more updates : )
Crystal clear 🙂 thanks 🙏
You are welcome 😊 Glad it was helpful!!
thank you, it's really helpful
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Good explanation
CART Algorithm uses Gini Index but you have implemented the dataset using entropy and Information gain so it will not be ID3 Algorithm?
Thank you for making it clear and concise, additionally can you please provide the source code ?
We are glad to have learners like you. Please do share your mail id so that we can send the notes or source codes. Do subscribe our channel and hit that bell icon to never miss an video from our channel
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!
wonderful
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 :)
Nice explanation. Could you please share the code, it would be helpful, Many thanks in Advance!
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its really awsm........
very helpful...
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hey great explanation .. covered all the topics neccesaary could you please 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.
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 :)
Thanks for great explanation
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Nice video. I was really helpful. Please can you send me the source code t practice
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.
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 informative video.
Can you share the code as our reference?
Thanks.
Thank you. Please share your email id with us (it will not be published). We will forward the source code to your email address.
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.
Hi, This is Surender, great explanation, can you please provide the python code.
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Best video!
What is decision tree with relearning of nodes?
Well explained thanks a ton
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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 :)
Awesome video. May I have the code that was used at the end of the tutorial? Thanks in advanced.
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amzng..sir
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??
Hey! Yes, You definitely can create.
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.
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!
hi could you please share the code..
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?
Kindly . Correct The thing
On timestamp 28:42
The last Entropy calculation should be of rainy but taken of sunny again.
Nice Video and great explanation .Can i get the source code pls
Thank you. Please mention your email id (it will not be published). We will forward the code 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, 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.
Very clear explain. May I have the source 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?
Great video. But could you please provide the source code. It will be helpful for us to study it.
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what did you import for doing tree decision?
Hi Nur, You have to first import the required libraries and datasets. Hope this helps.
The video is awesome, can i get the link to source code?
Hey Tanmay, we are glad you loved the video. Please do mention your email ID over here and we will send the files to you. Cheers!
great explanation..may i have the code
Thanks Obaid. Please share your email address, we will send you the code.
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
Thanks for the great video. Can i have the code please?
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hii sir...nice video please share the source code and dataset
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Nice video, may i have the code that was used at the end of the video.
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Great explanation. Can you share the code
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How we know that which algorithm is best for our data????
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.
awesome explanation...can you share the code?
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Can you please provide the code, it will be a great help!
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Thank you for the explanation. Can you please share the code?
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great tutorial, please can i have a code
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Very Well explained ..👍
Can I get code file??
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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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great help for me. could I get the code?
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Can u provide practice problems with solution.
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hi!! could you please share the link to python code explained in the video??
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Pls tell that how to print the decision tree .....
Hey Supriya, Yes you can. You will have to use sklearn and work though it in iterations. It is pretty simple. Happy learning!
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.
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 :)
Sir, can you please provide this code?