Random Forests : Data Science Concepts

[@azarel7]

Great video. 1) Spoke well and explained the concepts clearly 2) Threw and caught the marker every time, with no interruption in speech while doing so. Bravissimo!

[@jpark7636]

This is the best video to understand random forest in KZbin so far for me :))

[@saitaro]

2-hour lecture in 15 minute. ritvik rocks.

[@SPeeDKiLL45]

i swear

[@yuqingliu8412]

I swear

[@mikeshinoda2806]

1 semester in 15 minutes :)

[@MayankGoel447]

Thanks for the video! This is definitely the best explaination of Random Forest I have seen yet. I'm really enjoying learning Data Science from you

[@ritvikmath]

Awesome, thank you!

[@george1122ification]

love it ❤ Best explanation of random forest on youtube! thanks

[@mosama22]

I'm studying Data Science at MIT, you really can't imagine man how much "ritvikmath" is helping me, and a couple more channels, before I start any topic I like to tackle it first or just take a general idea, and you can't imagine how much your videos helped! Short, concise, and to the point! Thank you man 🙂 Just one notice, It might be a good idea to choose an easy to remember / clear channel name, sometimes when I'm talking to someone, it is almost impossible to remember the name of your channel, just a clear name with spaces! Thank you again 🙂

[@smishra115]

all hail the marker juggler and his short, crisp, easy to understand videos! Keep it up dude.

[@BO2Letsplay]

I'm trying to learn some ML content as it relates to classification to quite a large degree, and just want to say that this video on Random Forest is one of the only ones that actually made sense to me as a layman! Thank you

[@jimlanzi6802]

Very well organized and well put together. Simplified enough for the medium, but included just the right amount of detail to guide one in their further pursuits of the topic. Thank you.

[@eyayawb]

This is the best random forest explainer video I have come across. Thank you.

[@ritvikmath]

Glad it was helpful!

[@ashmitas]

thanks, well explained to a beginner like me. appreciate how a complex method was easily explained using a basic whiteboard and a relatable example.

[@qiguosun129]

Great lecture, help me recall random forest when I am learning the causal forest

[@edmundoguerramendoza7465]

Ritvik, once again you do an amazing job simplifying concepts in short periods of time, while still making them very understandable. Thanks!!

[@preetikharb8283]

This is THE BEST explanation of Random forest!! Thank you Ritvik :)

[@SuperRia33]

Feature importance section was an eye opener 😮

[@sourishguntipally8257]

This was an amazing video and super well made. It's astonishing how this material is free to learn from!

[@yuqingliu8412]

My favorate and best teacher in KZbin !

[@jarrelldunson]

Ritvik, hey, thank you... this was really, really helpful - a great explanation, Jarrell

[@ritvikmath]

Glad it was helpful!

[@juliocjacobo]

Concise and right to the point, as always. Thank you!

[@internetuser2399]

this is some high quality content. you deserve more views! great teacher.

[@Chillos100]

He’s simply the best!! Thanks for all your effort

[@t_geek9211]

Wow! You are really good at explaining stuff! That was amazing!

[@ritvikmath]

Glad you think so!

[@danspeed93]

First time I see this way of computing feature importance, thanks!

[@phungboston]

Big Thanks for a clear explanation!!!

[@storyteller1900]

This is an amazing class. It contains all the important parts of random forests.

[@haninalkabbani7766]

I can't describe how good your explanation is !!!

[@ritvikmath]

thanks!

[@MafaldaTeixeira-]

Great explanation! Thank you Ritvik!

[@janpieterwagenaar1608]

Ritvikmath, i would like to complement you with the clear direct explanation video's. you make it easily accessable and clear with practical examples. please keep it up. Kind regards, Jan Pieter Wagenaar

[@ritvikmath]

You are most welcome!

[@jasdeepsinghgrover2470]

Amazing video... You can also cover parts like random projections... That's something which can make them much more interesting.

[@ritvikmath]

That's a great idea!

[@jamesbrown6591]

This is the best explanation I’ve found, thank you 🙏

[@ritvikmath]

Glad it was helpful!

[@extcresources531]

This is gold.. pure gold!!

[@nelsonk1341]

Best DS KZbinr

[@loveena419]

Wow great explanation - I am hooked on these videos. Get the main points in a short timeframe - would be nice to have a video on Tuning RF and other ML algorithms. And the pre-req videos are very useful to have the right background to understand this one. Thank you!

[@joycwang]

great explanation much easier to understand

[@millenaalves4169]

what an awesome video! congrats, really helpful

[@hameddadgour]

Fantastic presentation!

[@Cassius-p7y]

Fabulously concise and accurate!!!

[@dinhnguyenvo3040]

You are godly easy to follow, big thank you from my heart

[@ritvikmath]

Glad I could help!

[@leoliao3389]

Thank you ritvik!! This video is so helpful!!

[@ForcesOfOdin]

Loved the interpretability of the random forests idea! Very clever / useful. I'm guessing that you would want to reshuffle the dth feature for each i to avoid the effect that the shuffled data accidentally correlates with an important feature.

[@NikBearBrown]

The algorithm described is random sampling, not bagging. Not bootstrap samples are being created as described.

[@karunasrees7402]

Thanks Professor , your explanation is very good. I am really enjoying your videos and they are helping me to focus on DS. I have seen many videos prior they only mention Idea 1 - Bagging and say it is Random Forests. But you have mentioned Idea 2 - Random Subspaces as well. Just to confirm on it , do the Random Forests use both the ideas ? Do you mean that Bagging + Random Subspaces = Random Forest ? If possible can you explain how to code it ? Thanks for your time on videos ! Many of your videos are good , even your Bias-Variance video is also super.

[@SethuIyer95]

Using associative rule mining and extracting all the rules from all decision trees we can interpret random forests.

[@shahab4804]

Hi. Correct me if I'm wrong, but Sampling in bootstrap method is with replacement. But the way you said it doesn't have replacement

[@roopanjalijasrotia3946]

This is great! How about a point or two about the pitfalls of using random forest for time series

[@kevinshao9148]

Thanks for the great video! Do you have a video or any recommend for RandomForest on Regression math derivation? Thank you!

[@squib3083]

Awesome explanation thank you

[@beshosamir8978]

u r incredibly amazing ,but i have 2 questions : 1- What is the meaning of when i use all features the tree will be correlated to each other, i know what is the meaning of 2 features are correlated ,but what is mean when i say 2 trees are correlated ????? 2- when i need to determine how much a specific feature is important now , i trained the model using 80% of the dataset and now do i get the accuracy of this (80% dataset) of the dataset and after that shuffle my specific column and get the accuracy again of 80% of the data after shuffling then subtract them ? or i'm using 20% for both ? but u said in the video u r get the accuracy of the data that made that tree so u almost talking about the 80% , it make no sense for me using 20% of the dataset

[@TheBalhamboy]

Just found your channel. Really well explained. Thanks :)

[@annikamoller7673]

what a great explanation, thanks man :)

[@ritvikmath]

You're welcome!

[@Fat_Cat_Fly]

amazing video!! really helpful, thanks!!!

[@ritvikmath]

Glad it was helpful!

[@keshavsharma267]

Thanks for the video. can you also explain interpretability via LIME and Shapely values?

[@ritvikmath]

Great suggestion!

[@samuelharris4509]

Why do we need the accuracy value on the 20% for each tree? Does that help with some weighted average?

[@geoffreyanderson4719]

Yo I heard the RF is bad alone and needs help when: 1) a strongly predictive linear feature exists in X. You gotta help the RF out by either feeding it the residuals from running the linear model on that feature first, so each model in the ensemble can do what it does best, the linear doing linear things and the nonlinear RF doing nonlinear things. Or else just preprocess to create an additional feature which is just the output of the linear model, and give the whole augmented feature set to the RF now. 2) 2nd order associations are expected to be important, because despite its subsampling of feature space, the RF is actually NOT good at automatically finding 2nd order predictive associations in X. THus we should help the RF out by doing some feature engineering of the 2nd order terms in advance into the X and then give it to the RF NOW. Further it might help still more by telling RF to stop using the typical 0.5 ratio default of subspace sampling and instead just focus on exactly 2 columns at a time, no more, no less, forcing it to look much closer at all the 2nd order associations that you expect should be found by the RF. These are hear-say and hypotheses. It would be cool to see how to do it in sklearn's pipeline on a dataset like "jewellery" which is used for demo code by the pycaret library. Jewellery has a strongly predictive feature "carets" or "weight" in its X. But they just look at trees alone in their model search, so I think it can be improved by helping out the fancy nonlinear tree models as described above.

[@vladimirkirichenko1972]

excellent vid! thank you.

[@trin1721]

Can't we get the feature importance for free, without permuting, by looking at the accuracies of models trained with and without certain features (in the random subspace step)?

[@ziaurrahmanutube]

Love your videos, very helpful and well explained

[@ritvikmath]

Happy to hear that!

[@Ostiosti]

Great video. But why permute on the training data and not on the test data? This should also show the importance of the feature, right?

[@beniborukhov9436]

I think that it's since we're trying to focus specifically on the importance of each feature to the model. We're avoiding adding the additional variable of how well the model generalizes and therefore works on the test data so we can see the features' contribution to the model's accuracy under ideal conditions.

[@YingjieWu-dt8vm]

great video

[@ahmetcihan8025]

This is it. Thank you so much .

[@beatss8286]

Thank you!

[@ritvikmath]

You're welcome!

[@manueltiburtini6528]

I love it!

[@TawhidShahrior]

you are a legend

[@neuodev]

You are aweomse!

[@KennieinKorea]

impressive, thanks

[@ritvikmath]

Glad you liked it!

[@warpathcucucu]

your're the goat

[@niknoor4044]

Great!

[@SuperHddf]

THX!

[@leroychiyangwa8320]

kkkk i like the entrance style

[@Fordalo]

you are goated

[@TheProblembaer2]

I love you.

[@Makako_Loko]

Oh my deus thank you