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

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** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training ** 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 Subscribe to our channel to get video updates. Hit the subscribe button above. Check out our Python Machine Learning Playlist: https://goo.gl/UxjTxm #decisiontree #decisiontreepython #machinelearningalgorithms - - - - - - - - - - - - - - - - - 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 - - - - - - - - - - - - - - - - - - - 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. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
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Text Comments (86)
edureka! (5 months ago)
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: http://bit.ly/2FBUtO7
Abhishek Ghaskata (15 days ago)
How we know that which algorithm is best for our data????
edureka! (12 days ago)
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.
japo neon (18 days ago)
CODE : https://github.com/random-forests/tutorials/blob/master/decision_tree.py
Nur Aisyah (22 days ago)
what did you import for doing tree decision?
edureka! (19 days ago)
Hi Nur, You have to first import the required libraries and datasets. Hope this helps.
Mustafa Bohra (26 days ago)
Best tutorial on YouTube!
Jaber Al-Shehri (1 month ago)
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
edureka! (1 month ago)
Hey Jaber! Thank you for appreciating our efforts. You can check out our Complete Machine learning tutorial here: https://www.youtube.com/watch?v=b2q5OFtxm6A&list=PL9ooVrP1hQOHUfd-g8GUpKI3hHOwM_9Dn Hope this is helpful. Cheers!
ِAsmaa Laila (1 month ago)
Thx .it's great
Sanya Kataria (2 months ago)
Sir, your video was very well explained but can you please share the code with me, as i tried copying the code and i am facing some errors. So please can you share the code.
edureka! (2 months ago)
Hey Sanya, Thanks for the compliment! 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.
Prince Singh (2 months ago)
Can i please get source code?
edureka! (2 months ago)
Yeah sure. Please share your email id with us (it will not be published). We will forward you the source code to your email address.
Pruthvi Sai (2 months ago)
Is the range of entropy 0 to 1 as shown at 23:28 ?
edureka! (1 month ago)
Hey Sai! Yes, entropy has a value between 0 and 1.
afia qamar (2 months ago)
Please share the code
edureka! (2 months ago)
Hi Afia! 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.
RoBoBoY (2 months ago)
I need code plz
edureka! (2 months ago)
Hey, Can you please share your email id with us (it will not be published). We will forward you thesource code to your email address.
Habiba Sani (3 months ago)
Nice video. I was really helpful. Please can you send me the source code t practice
edureka! (3 months ago)
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.
Rohan Nayak (3 months ago)
Can I get the source code
Abhishek Bhagat (3 months ago)
hi could you please share the code..
TWIN FRIENDS (3 months ago)
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??
edureka! (2 months ago)
Hey! Yes, You definitely can create.
manivarma indukuri (3 months ago)
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 .
edureka! (3 months ago)
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!
Tim Mak (4 months ago)
please share the code, thanks
edureka! (4 months ago)
Hey Tim, glad you loved the video. Please mention your email ID and we will send the files to you. Cheers!
Indrajit Mukherjee (4 months ago)
Awesome video guys. one small request can I get the demo code used here for my practice ?
edureka! (4 months ago)
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!
Atharva Karyakarte (4 months ago)
Can I get the code
Sumit Badola (4 months ago)
amzng..sir
Karthik Arg (4 months ago)
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?
edureka! (4 months ago)
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.
Ayat Ahmad (5 months ago)
can anybody tell that it is the code for decision tree or id3??
edureka! (4 months ago)
Hey Ayat, it is for decision tree. Cheers!
Terry Liu (5 months ago)
A great refresh of decision tree. Thanks!
edureka! (5 months ago)
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!
Shivani Sachdeva (5 months ago)
hi can you please share the code TIA
atharvav (5 months ago)
Right, Alright!
Santosh Birje (5 months ago)
very clear. Thank you so much :)
edureka! (5 months ago)
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!
Supriya Karmakar (5 months ago)
Pls tell that how to print the decision tree .....
edureka! (5 months ago)
Hey Supriya, Yes you can. You will have to use sklearn and work though it in iterations. It is pretty simple. Happy learning!
MrGeorgerocker (6 months ago)
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
edureka! (5 months ago)
Hey, Entropy is straightfoward and really simple to calculate. Can you elaborate?
monika singhal (6 months ago)
please share code
edureka! (6 months ago)
Hey Monika, we hope you liked the video. Please do mention your email ID over here and we will send the files to you. Cheers!
Anjali Sinha (6 months ago)
Hi..please share code
edureka! (6 months ago)
Hey Anjali, hope you liked the video. Please mention your email ID over here and we will send the files to you. Cheers!
Osama Ikhlas (6 months ago)
code?
edureka! (6 months ago)
Hey Osama, hope you liked the video. Please do mention your email ID over here and we will send the files to you. Cheers!
Ram Rana (6 months ago)
very nice explanation sir .........Great Thanks to You...
edureka! (6 months ago)
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!
TANMAY DHAUNDIYAL (6 months ago)
The video is awesome, can i get the link to source code?
edureka! (6 months ago)
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!
Vishwanath Ravula (7 months ago)
Hi, Could you guys give me some guidance to implement the decision tree algorithm in torch7 using Lua.
edureka! (4 months ago)
Hey Vishwanath, "https://indico.cern.ch/event/472305/contributions/1982360/attachments/1224979/1792797/ESIPAP_MVA160208-BDT.pdf This might be useful to you. Cheers!"
Monu Vishwakarma (7 months ago)
How did you decided the position of windy and humidity?
Abhay Verma (7 months ago)
please end me the code please my id is [email protected]
Sweta Pattanaik (7 months ago)
hi i have a question how did you calculated total entropy as 0.94.p[lz any one help
edureka! (7 months ago)
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!
Manshul Chhabra (7 months ago)
Can you share the code please.
edureka! (7 months ago)
Hey Manshul, please do mention your email ID over here and we will send the files to you. Cheers!
Raj Chauhan (7 months ago)
If the training_data is huge then how can we make the necessary changes and get the same correct output?
edureka! (5 months ago)
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
kalyan kumar (8 months ago)
suppose if I have some attributes and I have to find the root node.How to find the root node?
edureka! (7 months ago)
Hey Kalyan, root node is the node from which you can gain maximum information. So for detailed information on which node to select as a root node, you can directly jump to 26:38 mins. Hope this helps!
Pratima Boorla (8 months ago)
can u please share the code it would be helpful Prior thank you
edureka! (8 months ago)
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!
Arnav Aakash (9 months ago)
can u please send the code to me...I found your very video helpful but I couldn't find code anywhere in the description as pointed in the video
edureka! (9 months ago)
Hey Arnav, glad you loved the video. If you could please mention your email id(we won't publish it) so that we can share the files with you.
Souman Paul (9 months ago)
Anyone with no prerequisites will able to understand Edureka all classes.
edureka! (9 months ago)
Hey Souman, that is our aim. Thank you for appreciating our efforts! Keep supporting us, cheers :)
Rishi RD (10 months ago)
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
edureka! (10 months ago)
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 :)
vignesh viki (10 months ago)
code explanation should be slow as it is a key area just moving ver fast
Suhas Nayak (10 months ago)
What are computational complexity for Decision Tree? Can you please give analysis for tree training phase and prediction phase please.
Digi Bro (10 months ago)
where is code in description
edureka! (10 months ago)
Hey Digi Bro, mention your email address and we will send it over. Cheers :)
Sai Saran Gangisetty (11 months ago)
Have few doubts...can Any kne clarify
Sai Saran Gangisetty (11 months ago)
edureka! I mean to say can I calculate the probability for my train data set and test data set based on individual??
Sai Saran Gangisetty (11 months ago)
edureka! In the above video explanation was superb and appreciated . But for the same above example how can I calculate the probability for each user or each id after building the model?
edureka! (11 months ago)
Hey Sai,sure. We will try helping you out. You can mention it here itself. Cheers :)

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