Decision Tree In Machine Learning | Decision Tree Algorithm In Python |Machine Learning |Simplilearn

🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): www.simplilearn.com/iitk-professional-certificate-… 🔥AI Engineer Masters Program (Discount Code - YTBE15): www.simplilearn.com/masters-in-artificial-intellig… 🔥AI & Machine Learning Bootcamp(US Only): www.simplilearn.com/ai-machine-learning-bootcamp?u… 🔥 Purdue Post Graduate Program In AI And Machine Learning: www.simplilearn.com/pgp-ai-machine-learning-certif…

We hope this video was useful. The link for the dataset used in the video is provided in the description. Thanks!

Thanks for you clear explanation about what is decision tree. Is it mandatory to learn python language in order to more understand ML? What I only know and am comfortable with are Swift / C/ C++/ Obj-C.

Excellent tutorial on Decision Tree Classifier. Thanks a ton. It was awesome.

This tutorial was precise and clear. I loved it. Thank you.

Like the demonstration and visualizations. Simple and Clear. The Entropy formula of getting 0.57, I believe is using log base of 10. I tried log base 2 got around 1.91. Thanks for the Video

Just Woooow,the way you teach , simple and perfect!

Is there any link to see after we have trained the decision tree , how the model will run for another set of raw data? Such as, for the training, the result is provided in excel sheet but if it is not provided how the trained decision tree will predict?

Nicely done ! You have a like and a new subscriber.

My question, the model is good and I understand what you have done. However as a newbie, how can I actually see the tree instead of seeing just accuracy scores everywhere if you understand what I am trying to say. As in, is there any way I can actually visualise the graph? Also how can I append my Y predictions to whether the person will be approved a loan to my original dataset? I am seeing everywhere people doing the usual sci-kit learn train test, modelling predictions and checking of the accuracy, but I don't see anywhere of people actually inputting one row of the data and seeing whether the person should be approved of a loan or not. Are you able to help me on this? I can pm my email if needed.

Awesome tutorials by Simplilearn. I am a learner right now, and you guys are helping me a lot. Thanks a ton. Would request for the data set to practice.

Let me say thank you so much for this huge valuable work. It is so easy, you are a professional teacher.

I guess you should add minus sign before the entropy formula.

Hello. Nice explanation. Could you please send along the data set you used for the Use Case? Also, I have a couple questions about the disadvantages of a decision tree. You say that the decision tree is high variance and low bias (at 9:02 minutes into the presentation). #1. I am not sure why low bias is a disadvantage. Why is that? #2. I assume that the closer the decision tree gets to zero entropy (i.e. each leaf contains only one class of data), the higher the variance and the lower the bias. Is that correct? #3. I guess that the art of building a decision tree is finding the best sequence of splitting criteria to employ, and knowing when to stop splitting. I guess you can always continue to split until you reach zero entropy, but toward the end of that process you are just learning noise (overfitting). I assume that choosing the "best" sequence of splitting criteria essentially determines how soon you start learning noise. Please comment on these three questions if you would. Thanks

thank you, really liked how you brake down each step to understand how the code works. Thanks

Helped me so much for my machine learning project1!!11 Thank you so much!

Well explained. Enjoyed and got to understand decision trees better. God bless you all!

Great explanation, simple to understand thank you!!!

Thank You! I received all the required data sets.

Awesome tutorial. Simple and Clear explanations.