Code With Me : Decision Trees

[@andyw1725]

pls do more of these coding videos! As a beginner I really learn a lot from your thinking and coding process

[@ritvikmath]

More to come!

[@CptJoeCR]

I agree!

[@040040919]

I'm not a beginner and I learn a lot as well.

[@adamcaudell1562]

We have been looking at decision trees under the hood for 3 weeks at Uni and you taught me more in 15 minutes. Thanks for the videos!!!

[@taylorparrish4323]

More code with me pls! It's very helpful seeing you solve the problem in real time as well. Seeing your thought process really helps with the intuition.

[@taylorparrish4323]

additional note: coding from scratch with no heavy computation libraries (or minimal) is a brilliant way to drive the concepts home. love it

[@supakornsr4392]

This video really helps me understand the concept and the steps, although I am only using R. The length of the video and the amount of the contents are also perfect.

[@goitomyacobb.2447]

Simple & elegant. Kudos to you buddy.

[@anuragsangem1442]

Thank you for this. I'll always learn something new in every single video of yours , please do more such videos.

[@sharmakartikeya]

Coding from scratch is the only thing I needed! Thank you ritvik ☺️

[@tiger06t]

Thanks! This helps a lot! Really like your tutorials, because you make all this concept simple and easy to understand.

[@ritvikmath]

Glad to hear that!

[@Uma7473]

Thank you... for making us to learn, think and code.

[@chibuikejibuaku9956]

You're a good teacher. We should see more of this.

[@process6996]

Awesome video! I didn't quite understand how to do the splits based on entropy but now I do. Thank you!

[@james-barnett]

Once again another great lesson!

[@ritvikmath]

Glad you liked it!

[@anaydongre1226]

Great explanation. Thanks for the coding session !!

[@ritvikmath]

You are welcome!

[@netanelmad]

Thanks! I love it. Maybe add some more explanations here and there for beginners :)

[@ritvikmath]

Thanks for the kind words! I'm always looking to improve; I'm wondering if you have some specific examples of what I could have provided that would be helpful for beginners?

[@netanelmad]

Thanks for replying! Well, speaking for myself, I have some background in python, numpy and pandas, but not really in decision trees and data science concepts in general. I would like it if you explained (in a few words) what's the big picture and our goal in making decision trees. Also I don't totally understand what's the entropy's meaning and role in this context. Again, I'm speaking for myself obviously.

[@netanelmad]

BTW I'm enjoying your channel and explanations. Very nice.

[@ritvikmath]

@@netanelmad Thanks for the valuable feedback!

[@shubhampandilwar8448]

Great thinking process. I am trying to code some machine learning algorithms from scratch. This one helped immensely in my thought process. I am struggling to code logistic regression from scratch. Will you please help? Please do more of these how to code machine learning algorithms from scratch. As a beginner, it would help me and many others like me hugely.

[@ritvikmath]

Glad it helped! And that's a good suggestion!

[@elkamel]

Excellent explanation. Keep it up..

[@ritvikmath]

Glad you liked it!

[@kaushikjayaram8556]

Great tutorial! Keep it going. Thanks

[@theoreticalmindset]

Really really good explanations!

[@user-xi5by4gr7k]

Excellent video!

[@ritvikmath]

Thank you very much!

[@afiqyahya3398]

Damn. Such that easy. Nice

[@wast1297]

How would you automatize the last part of updating the dataframe according to the best split and getting a new dataframe?

[@BhuvaneshSrivastava]

Now this is really great !! As always, really informative video as you actually went inside the code of decision tree. Just a Suggestion: It would be nice if you also show the python library (DecisionTreeClassifier) based implementation of decision tree and campare the results. Also, is there a way for me to see the actual decision tree which gets created using decision tree classifier?

[@ritvikmath]

Glad you liked it! And thanks for the suggestions; comparing our results to the built-in would be a good sanity check.

[@giorgikokaia7200]

Great explanation. Can you please share the csv file to run code?

[@ritvikmath]

Thanks for reminding me! I just added it to the description

[@4767039]

I had trouble with the code when I used the fish column defined as 'type' I got all zeros, but no error message. Since type is reserved maybe? So I had to change the column name to "fish_type" ...perhaps there is another way around this. Otherwise, it worked fine after that.

[@ritvikmath]

ahh thanks for the note and glad the rest of the code worked out!

[@sushilchauhan2586]

Pls make such videos

[@Phil-oy2mr]

Cool video! It may have been helpful to see how it runs with different steps sizes

[@ritvikmath]

Glad it was helpful! Good suggestion

[@fengjeremy7878]

So clear!👍

[@chenqu773]

So, after the 2nd split (using weight > 4), you used again the length feature for the 3rd split. Does that mean that the same feature can be reused in the nodes of decision trees? Would it lead to over-fitting ? Many thanks

[@Greatasfather]

Learnt 4 hours lecture in 20 mins,

[@tuamiga-amigo]

anyone has the coding decition three to detect sarcasm?