Machine learning - Random forests

[@lwoltersyt]

I simply love all explanatory video's of Nando de Freitas; clear and effective. Straight to the point

[@siddarthjay3787]

Excellent video. Professors like him are the reason learning is fun. They throw exciting ideas at you as if they were no thing.

[@breckenpeter5343]

I guess im randomly asking but does anyone know a trick to log back into an instagram account? I somehow lost my account password. I appreciate any tips you can offer me.

[@arthurlayne6350]

@Brecken Peter Instablaster ;)

[@breckenpeter5343]

@Arthur Layne I really appreciate your reply. I got to the site on google and I'm trying it out now. Seems to take a while so I will reply here later with my results.

[@breckenpeter5343]

@Arthur Layne it did the trick and I actually got access to my account again. I am so happy! Thank you so much, you saved my account :D

[@arthurlayne6350]

@Brecken Peter You are welcome :D

[@meganmaloney192]

Incredibly helpful! Thank you for posting.

[@comadano]

Thanks for posting these great lectures to be publicly available. I hope the remaining lecture videos get posted soon!

[@ApiolJoe]

The ressource that helped me the most in least amount of time. Thanks for sharing.

[@helenlundeberg]

Around 1:08:25, the professor talks about Bayesian optimization using a GP prior or something. What I'm not clear on is what is the prior on ?

[@kellyli1920]

This is so great clear and easy to understand!!! Thank you so much!!!

[@junfu8695]

+Kelly Li it is

[@drbhojrajghimire3908]

Very good classroom video which is very simple, interesting and clearly explained.

[@LuisFelipeZeni]

Excellent professor, thanks for sharing your Knowledge with us.

[@alefranc100]

Tks Nando ... This lecture is really good and useful !

[@GoBlue7171]

Wow this is amazing. Such a clear and informative lecture.

[@rodrigo100kk]

This course is amazing !

[@leicaandrei]

why is the bias very low at 40:20?

[@shervin0]

I'm not completely sure, but it could be because of the way trees are created. They try to maximize information gain with simple decisions. Due to the simple decisions and use of the information gain cannot cause very biased divisions (As I said, I'm not sure if this is the reason! :) )

[@antonosipov100]

Very good introduction to Random Forest. Thank you!

[@tonyperez8878]

how to compute the info gain? what is the relation of it with the entropy and mutual information ?

[@hyperzoanoid]

Do random forests need classification trees with more than 1 node to be effective?

[@kaleeswaranm2679]

Very good lecture, but I would suggest watching in *1.5 speed.

[@cfelixcfelix6568]

I did not get the thing with the 3 trees and the historgrams. Does not each tree give a definite answer?

[@AbhijeetSachdev]

+Cfelix Cfelix Ofcourse it will give, but in case of classification you will just take the sign of average value. . which is exactly the same as majority. In case of regression, "averaging" actually comes into account

[@nicooteiza]

+Cfelix Cfelix Actually, not necessarily. In many real-life cases, data is not separable on the features, so even a very big tree would not be able to give definite answers. Moreover, each individual tree could be "pruned" to a certain depth, so the answers would not be definite classes, but "probabilities" for each class. For example, in a 3 class problem, for each tree you would have a [p1, p2, p3] result, where p1+p2+p3 = 1, and the final prediction can be obtained by adding all the tree's vectors element-wise and dividing by the number of trees.

[@EmreOzanAlkan]

Thank you! It's really nice and easy to understand!

[@jiansenxmu]

A Perfect and Amazing Lecture on Random Forest, dank u wel !

[@PrakashMatthew]

Thank you @Nando de Freitas. Is the second part of the lecture, the one to be taken by the grad student, available online?

[@satter87henne]

I looked up the ressource (Criminisi) and I wonder: Is there an R package or a Python package where this specific algorithm is implemented. There are many R packages and I know there is sci-kit learn in Python. However, I want to make sure I use the more general model as outlied by Criminisi.

[@fangliren]

I'm not sure I understand your question; with only one node (which would have to be the "parent" node, by definition), the tree wouldn't have any "children" nodes into which to sort the data based on the criterion in the parent node. The tree wouldn't do anything at all... Perhaps you are asking a different question?

[@tadinglesby8997]

Can one have a greater level of tree depth in their model (say D=10) with only 8 features. Or does it have to be a a maximum depth of 8 corresponding to 8 features?

[@ecemilgun9867]

You can actually use a feature in more than 1 splits. It sometimes causes overfitting but is sometimes needed: For instance both deficiency and abundance of glucose in blood levels indicate a disease.

[@fatemehsaki9020]

Really Great lecture and teacher !!! Thanks so much............

[@shubhamjha1]

The next lecture(the one about kinect and stuff) seems pretty interesting. Could i have a link to that?

[@zenzafine4500]

did u get the link?

[@zenzafine4500]

i think is this one kzbin.info/www/bejne/l4nUenacfZmNoqM

[@ravimadhavan1984]

Great lecture!

[@agnerraphael]

Thanks for publising, Great Help for my Project

[@sameenatasneemshaikh8349]

Thank You sir excellent lecture please share other videos like you said Bayesian optimization lecture please share that lecture also.

[@PravinMaske1]

excellent video. very helpful easily understood...

[@reneveloso]

Thank you! Great lecture! You have a very brazilian name... :-)

[@tpinto9]

Portuguese?

[@sheikhsaqib6323]

Can anyone here please help me out, am trying to classify accelerometer and gyroscope values of a robot using random forests. The classification labels are walking and under peturbation using diffenrent forces. From what i understand this is data that evolves over time as the robot walks. What my question is can i use the technique described in the video to do the classification or do I need to do it a different way(if so please point me to the right material where i can read about how to classify such data)

[@aidaelkouri7050]

Hi! Loved the video. Was wondering how the problem would work if you chose greater than 2 features to look at? Would you plot them in the 3d then? Thank you

[@RobMartin-gz3zk]

yeah if you wanted to visualise it you could plot it in 3d. but all the algorithm is doing is seeing how many points are within a certain region, for example if you have n features then you can pick one of them and see how many points are above or below a certain value for that feature. the algorithm itself doesn't need to plot anything to work

[@RobMartin-gz3zk]

sorry didn't realise this was 7 years ago lol

[@aadilraf]

Amazing lecture!

[@rezayousufi4200]

Super explanation!!!!

[@BoredFOMOape]

Thanks for posting! Great lecture

[@d0msch]

thanks for publishing, helped me a lot :)

[@deepakk1944]

What is bagging?

[@sourceschaudes9587]

boosting

[@theamazingjonad9716]

Bagging is an ensemble technique whereby you randomly build k models from k subset data from the original data. The subset data are built using bootstrap resampling.

[@BangsterDK]

Amazing. Thank you so much!

[@abhijeet24patil]

in step 2 of algo Random Forest : "until the minimum node size n_min is reached."-i dint understand this line. when we should stop Selecting m variables at random from the p variables.

[@BudiMulyo]

Thank you,, ! It's all very clear posting video.. !

[@ronthomas8331]

excellent lecture!!!!

[@aaronbrinker2613]

Nando for President

[@jihoonkim6819]

Thank you for the nice lecture.

[@AbhijeetSachdev]

Brilliant lecture :)

[@fathiafaraj479]

Hello sir: I am a graduate student in computer science My scientific research (object detection using random forest and local binary pattern ) I hope you help in RF code ;i useing MATLAB

[@andreiherasimau7800]

excellent video!

[@R1CH4RDGZA]

Loquacious, now that's one good obscure word just off the cuff

[@alkodjdjd]

Te enviei correio e nao obtive resposta. Obrigado

[@KrishnaDN]

My dream is to work with professor like him

[@yuzhou1]

great video, watch at 1.5x speed

[@jennifermew8386]

Thank you

[@张超-y3w]

great!

[@donnasara70]

Спасибо, что без акцента

[@melaxxl]

walla shaza.

[@kikokimo2]

play in x2 speed!

[@muneebaadil1898]

Although I appreciate giving away the knowledge and trying but I'm sorry, VERY slow lecture, leading to a boring class.

[@prathameshaware7433]

Yeah..it was slow but anyways there is an option in KZbin to increase the speed so u can use it next time :)

[@dinofranceschelli3969]

Excellent ! Really easy to understand thanks for sharing !

[@naiden100]

Thanks for a great lecture!