Machine Learning Lecture 29 'Decision Trees / Regression Trees' -Cornell CS4780 SP17

Lecture Notes:

[prattzencodes]

With all due respect to Professor Andrew Ng for the absolute legend he is, Killian, you sir, are every ML enthusiasts' dream come true. 🔥🔥🔥🔥🔥

[AnoNymous-wnfz]

15:13 introducing Gini impurity
23:50 KL algor
46:00 Bias-Variance discussion

[orkuntahiraran]

This is perfect. I am coming from a non-technical, non-math background; and this presentation really made me understand DT easily. Thank you very much.

[jalilsharafi]

I'm watching this end of December 2021, I found the demos at the end starting roughly at 45 mins in the video very informative about the capabilities and limitations of a decision tree. Thanks.

[silent_traveller]

Hats off to you sir. This series coupled with lecture notes is pure gold. I have watched several lecture series on youtube till the end but wow this lecture series has the most retentive audience.

[abhishekkdas]

Thanks Professor Kilian Weinberger. I was looking for a refresher on the topic after almost 5 years and you have made it as easy as possible :) !

[varunjindal]

Thanks Professor Kilian Weinberger. Examples in the end was really helpful to actually visualize how trees can look like.

[khonghengo]

Thank you very much, Prof. Weinberger. I was reading The Elements of statistical Learning as my reading course, then I found your channel. I truly appreciate your lectures also your notes, I print all of your notes and watch your almost all of your videos, they are extremely helpful. Thank you, I really appreciate that you let us have access to your wonderful lectures.

[cacagentil]

Thank you for sharing your content.

It is very interesting. Especially the discussion about why we do this ( computational problems, NP-hard, people tried many splits and found out it was the best in practice), the interactive examples at the end (very useful for learning) and all your work on trying to make it clear and simple. I like the point of view of minimizing the entropy from maximum the KL between two probability distributions.

In fact, it is also easy to see the Gini impurity loss function as an optimization problem in 3D also (you get a concave/convex function by computing the hessian matrix with two parameters as the third one is just 1 - p_1 - p_2) and you have to optimize it on a space (conditions on the p_i) and you can actually draw the function and the space. You get the maximum/minimum at 1/3 for p_1, p_2, p_3 (what we don't want) and it is diminishing as we move away this point (with the the best case for one which is 1 and the others 0).

[TrentTube]

Kilian, is there some way I can contribute to you for your efforts in creating this series? It's been fantastically entertaining and helped in my understanding of these topics profoundly.

[geethasaikrishna]

Thanks for awesome lecture & your university for making it available online <3

[nicolasrover]

Thank you very much. Your teaching is incredible

[KulvinderSingh-pmcr]

"No man left behind", wait .. that's Decision trees right ??
Thanks prof. Enjoyed and learnt a lot!!

[michaelmellinger]

Decision trees are horrible. However, once you address the variance with bagging and the bias with boosting, they become amazing. @12:50

[KW-mdbq]

Should probably have mentioned the log used in Information Gain is Base 2.

[zaidamvs]

i have a question how we know the best sequence of features that we should use in each depth layer because if we want to try each one and optimize with 30 to 40 features will take forever, or how we can do this for m features because i can really visual how this work.

[hohinng]

28:24 this sound like a horror movie lol

[michaelmellinger]

@34:28 Can view all of machine learning as compression

[shaywilliams]

Forgive me if I'm wrong but if a pure leaf node with 3 classes that results in P1=1, P2=0, P3=0, the sum of Pk*log(Pk) would be 0, so the idea would be to minimize from the positive entropy equation?

[rahulseetharaman]

Why do we do a weighted sum of the entropies ? What is the intuition behind weighting them and not simply adding the entropies of the splits ?