How to Prune Regression Trees, Clearly Explained!!!
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- เผยแพร่เมื่อ 10 ก.ค. 2024
- Pruning Regression Trees is one the most important ways we can prevent them from overfitting the Training Data. This video walks you through Cost Complexity Pruning, aka Weakest Link Pruning, step-by-step so that you can learn how it works and see it in action.
NOTE: This StatQuest assumes you already know about...
Regression Trees: • Regression Trees, Clea...
ALSO NOTE: This StatQuest is based on the Cost Complexity Pruning algorithm found on pages 307 to 309 of the Introduction to Statistical Learning in R: faculty.marshall.usc.edu/garet...
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0:00 Awesome song and introduction
0:59 Motivation for pruning a tree
3:58 Calculating the sum of squared residuals for pruned trees
7:50 Comparing pruned trees with alpha.
11:17 Step 1: Use all of the data to build trees with different alphas
13:05 Step 2: Use cross validation to compare alphas
15:02 Step 3: Select the alpha that, on average, gives the best results
15:27 Step 4: Select the original tree that corresponds to that alpha
#statquest #regression #tree
NOTE: To apply this method to a classification, replace SSR with Gini Impurity (or Information Gain or Entropy or whatever metric you are using).
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I read some reference books like Introduction to Machine learning and Hands on Machine learning, but I didn't find any details about Decision Trees, or lets say other methods too like you covered!! Could you suggest some more references to get more deeper understanding?
I really wanted to try out the offline material too, but I'm still a student and can't afford it
And, Thanks a lot, this is really the best place for me to learn about machine learning as my first source, the explanations are both deep yet still accessible to a beginner
@StatQuest I am not able to understand how cost complexity pruning will work with classification. You are saying replace SSR with entropy or Gini index but how it will be calculated for leaf nodes. in SSR we are taking difference between actual and predicted (from leaf node), square it and sum it. In classification, we have predicted classes at leaf bode. Should we use predicted classes or actual classes for calculating gini index or entropy. I am confused. Please let me know.
@@pradeeptripathi7366 We use predicted and actual classes to calculate gini. For details, see: th-cam.com/video/_L39rN6gz7Y/w-d-xo.html
The channel with the lowest Gini score of likes vs. dislikes
You get a DOUBLE BAM for that comment. Funny! :)
Josh: you have truly cracked how to use technology(slides/basic animation) to change the way we are teaching for decades. I wish all universities take a note from you and revise the way they are teaching
Thank you very much! :)
yes, 100% true, lot of knowledge sharing just with simple visualisation, as you mentioned, way of teaching matters a lot.....Tks to our MASTER Josh Starmer once again for this awesome video/content !!!
@@statquest I can confirm this. I am pursuing a Masters Degree in Data Analytics Engineering. And I have this course that is giving me a headache: Statistical Modelling.
Your videos have helped me a lot, this is the the perfect stuff I was looking for. BAM!!!! BTW, I saw the video where you explained how your pop helped you and the StatQuest community in general, I totally get it why your videos are perfect. Double BAM!!!!!
Seriously man, thanks a ton.....
seriously yes! I'm taking an online course from MIT....brilliant faculty but just oh so removed from the every day regular experience of learning as a student. Their slides, and teaching methods leave much to be desired. I don't understand anything they are teaching in stats or ML but Starmer is saving my life atm.
This is the best explanation of regression trees that I could find online. Professors are always too mathematical and programmers are too practical. You're explanation is juusssst right. Thanks a bunch for this!
Thanks!
Absolutely brillian videos!!! I watched everything from the 1st one to this one in the list and understood so many things that I never understood in schools. I love your videos so much!
Awesome, thank you!
So good! Consistently high quality across across videos and time! Keep going. Many thanks!
Thank you very much! :)
lol, that intro one who watches friends and Stat Quest would get it!! love your content its the best machine learning tutorials available
Thank you very much! :)
I searched lot of thing for my project on ML to start from scratch.
Then i landed here
You nailed it. 🔥🔥🙏
Now i am on edge of completing my project
thank a lot
Awesome! Glad my videos are helpful. :)
Re watching after practising I can even further appreciate the quality of your explanations, thanks Josh :)
Thank you very much! Good luck with your practicing. :)
I love the way you say "BAMM"!!! Gives great relief during the video :) I want to say your style of teaching is great. The way you are explaining is making very easy for us to understand. In my opinion I can say "A difficult subject with easy to understand using your video lectures!" Thank you very much.
Thank you! 😃
This video really helped me to clearly understand the concept. Thank you for this good work
Awesome! :)
This is awesome. I remember I was a bit confused when I was reading tree based methods in An Introduction to Statistical Learning. This really helps me understand it much easier when I can visualize it other than read some formulas. Thank you!
Hooray! I'm glad the this video is helpful. :)
I am reading the same book now and without this video series it's impossible to understand.
Thank you very much for this video! I really enjoyed the full step-by-step process of building the various trees using different alpha values and the use of cross validation to select the best alpha!
Double bam! :)
Best video on pruning and tree selection till date!!!!!
Wow, thanks!
Oh my Buddha!
I'm falling in love with funny of your voice when you're explaining.
Before I met your channel, my head is spinning round and round.
I don't know what to do with my learning, but you came in and took me by big surprise.
You made the abstract concept to be simple!
Thank you!!!!! :)))))))
Wow, thank you!!!
Thanks, Josh Starmer. The way of using train + test data to find a list of alpha, then use K-fold CV on train data to find out the optimal alpha leads to the data leakage.
Noted
Thanks, Josh, every time I watch your video, I feel like the concept is very easy to understand! lol
Happy to help!
As always the clearest explanation! Thank you so much 😊 and BAM BAM BAM
Thank you! :)
What a beautiful content!
I'm not an English speaker, but His video is more helpful than the Korean lecture provided by the college I attending.
bam! :)
thank you Josh for your very easy understanding explanation & lovely rhythm
Thanks! :)
Excellent explanation, you are the master in teaching, thank you so much much for your valuable effort
Many thanks!
sir i am learning ML from your videos and everyday i am forced to comment expressing the beauty with which the concept is explained..and the best part is you still clear our doubts even after 3 years..for those who don't know sir has also written a book which is too good
Thank you very much! :)
Sir can i connect with you on linkedin
@@pratyanshvaibhav Linkedin limits the number of connections anyone can have, and I've hit that limit.
Okay sir
Really love your content. Must watch content for any learner
Thanks!
The good thing about his videos is you just have to watch any video once and the concept will not leave you for a long time.
Awesome! :)
As always awesome! Thank you Josh!!! Horraaaayyyy
Thanks! :)
The way you explain..and the amount of effort you put in the videos is great. I have learnt a lot from you sir. I always feel so positive and motivated while learning from you. Thank a lot🎈
Thank you! :)
love the reference to Phoebe ! also thank you, all your videos are very helpful
Glad you like them!
It is such a great explanation. Super helpful! Clear and fun! Really appreciate your time in making the video. Thank you!
Glad it was helpful!
Damn. I was finding only scientific articles and I was having trouble understanding the CCP, now you made it very clear! Thanks.
Hooray!
The Best! The Best! The Best! video I've ever seen about Tree Pruning.
Thanks a lot. Now I got the concepts.
BAMM!
Hooray!!! Thank you very much! :)
Thank you so much for posting this!
Hooray! :)
Wonderful explanation. Thank you very much
You are welcome!
This is brilliant work! Thanks a ton!
Thanks! :)
BEST CHANNEL! no i m not just saying, i m shouting!
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Explanation is TRIPLE BAM!!!
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Liked and Commented to help you with the TH-cam algorithm.
Triple bam! :)
Love the song in the beginning!!
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Hi Josh, those explanatory vidoes are incredible. Thanks for the great work!
Thank you!!!
Best intro of all the statquest videos which I have seen 😍
Hooray! :)
Thank you so much for this amazing video. Very Amazing!
Thank you!
Ahhh Phobeee from Friends aka Smelly Cat. Haha good one Josh.
Thanks! :)
excellent explanation. thank you
Thank you!
Josh, the videos on this channel are nothing short of superb. I have only one suggestion: how about a dark theme for these presentations? That white background is like a supernova, especially on my 55" TV.
This channel is gold mine i am telling ya :D
can you cover box cox theorem (power transformations)
Your intros make me smile :)
Thank you! :)
we love you DOSS.. hope me too will surely one day be a patreon member
Great explanation!
Thanks!
I don't know why I spend a lot of time googling if I always end up watching statquest haahahha
BAM! :)
(Making notes for my own future reference)
Tree Score, SSR + αT, is used to create a set of Prune Trees.
Then, apply data (cross-validationally), to all Prune Trees.
The Prune Tree (and its α value) that give lowest SSR (with testing data set) is the winner.
Noted
greate , thank you for your work . very clear
Thanks! :)
Thank You !! Just perfect :)
Thanks!
you are awesome! clear! to the point!
Thank you! :)
Thanks so much for your video.
I've watched 8-times and have still one question.
It is about 13:17~14:08
Could you possibly explain more details about the sentence
13:17 “Use the alpha values we found before to build trees(full and sub) that minimize the tree score”?
My questions about this sentence are
1. How can we use alpha in building trees process?
- I thought the way we build trees(full and sub) is the same as how we did in your video ‘regression tree’
*I understand tree score, alpha, and how alpha plays role in changing the tree scores of different size trees(full, sub trees)
my question about the role of alpha(from whole data) in creating trees..
2. If we can build trees with the same way as we did in ‘regression tree video’, why do we need this process of 13:17~14:08?
The idea is that when alpha is 0, we build a full tree just like in the original regression tree video. Then we increase alpha to the first value and that causes us to prune that tree a little bit. Then we increase alpha some more and then we prune the tree some more, until we've used all of the values from alpha we identified earlier.
Hello Josh, Thanks for this amazing video, I am implementing cost complexity pruning on the basis of this video. Although I have one question: How do you build a decision tree using a particular value of alpha using the training data (during cross fold validation)?? How does alpha help?
I am working on classification decision tree, here's what I do:
1. Use all data to build full tree, get all subtrees and for every subtree get a value of alpha.
Missclassification error of one subtree = sum of gini impurities of all leaf nodes
2. Divide data into 10 folds, for each fold:
- build decision using each value of alpha and training set. How? What role does alpha play here? I can grow a tree and then get subtrees without alpha
- calculate test error (1-accuracy) for each subtree.
- select subtree, represented by alpha having the lowest test error.
3. selected alpha = avg alpha across all folds
4. Pruned tree = tree that has the alpha = selected alpha
I apologise if this is a stupid question :)
Say like the full sized tree has 12 leaves and we are restricting ourselves to building a tree with only 10 leaves. Which 2 leaves should we remove? Alpha helps answer that question. We want to remove the 2 leaves that will give us a better tree score than the original tree.
@@statquest Thank you Josh! My problem is solved. Thank you again for this great video. I hope you know that this is the ONLY resource out there that explains cost complexity pruning so nicely.
@@hemlatasharma5288 Thanks!
Thanks Josh!
Thanks! :)
Your channel is amazing man. Great job
Thank you! :)
Thanks a lot for this. I came here after getting confused reading this concept from a book. I am inspired by your teaching style. Your style of teaching by examples is the best way to transfer knowledge without losing the audience at any point.
May I ask how much time do you spend to create a tutorial like this? Also what kind of tools do you use to make these videos.
Each video takes a long time - maybe 6 weeks or more. And I talk about how I do everything in this video: th-cam.com/video/crLXJG-EAhk/w-d-xo.html
Hello,
first of all thanks for the great material you produced and shared, certainly among the clearest and effective I've come across.
My questions are about the cross-validation trees to determine the right alpha values.
As a premise, if I understood correctly, we first determine candidate alpha values by :
a) create a "full" tree from the full training+testing datasets
b) produce the corresponding family of "pruned" versions (and I guess asses their SSRs in preparation for the next step) based on the morphology of the "full" tree (meaning, all possible pruned trees are considered - is that correct?)
c) identify the candidate alpha values as those by which the "full" tree's score becomes higher than one of the pruned versions.
Assuming the above is correct, when we move on to cross-validate in order to ultimately determine the right alpha, I understand that we resample a training set (and a corresponding test set) for a number of times.
Each time, we build a new tree from the training set, and its associated set of pruned versions (let me call these tress a "cross-validation family of trees" (CVFTs)), and assess their SSRs based on the test set for the current round in order to contribute to ultimately calculate the actual alpha to use.
First question: how come every CVFTs in your slides has a number of members that equals the number of candidate values for alpha?
couldn't a resampled training set might give rise to trees with more or even fewer leaves - and corresponding pruned versions - than the tree that was used to identify the candidate alpha values? And in that case, the candidate alpha values might be in larger or smaller number than the possible number of trees in the CVFTs at hand.
I imagine that a possible answer is that the number of members in a CVFTs can actually be different than the number of candidate alphas, and that the pruned tress in a CVFTs are actually identified through their Tree Scores when each of the alpha candidate values is applied -- and if so I guess the issue is that perhaps this mechanism does not stand out 100% from the presentation...
Second question: if we assess the trees in each CVFTs only by their SSRs, wouldn't always the tree with more leaves (therefore alpha=0) win?
Thanks much
What you wrote for b) "all possible pruned trees are considered" is not correct. When we remove a leaf, we don't just create all possible subtrees with one leaf removed. Instead, we pick the one subtree that, when we remove one leaf, results in the smallest increase in the sum of squared residuals.
@@statquest
Josh, OK, that makes sense -- so this is repeated on each new subtree to produce the set of trees where the candidate alpha values are then formulated as at minute 13:03, correct?
If so, are my subsequent questions still standing?
@@stedev2256 Each time we do cross validation we get a new "full sized tree", which may have a different size than the original. We then use the pre-determined alpha values to prune that new tree and use the test dataset to find out which tree (and alpha value) is best for that iteration.
As for your second question, this is where the "testing" data comes in handy. A full sized tree with the most leaves (and alpha=0) will probably overfit the training data, and thus, do a pretty bad job predicting the testing data. So in practice, the full size tree (with alpha = 0) performs great with the training data (low SSR) but poorly on testing data (high SSR).
@@statquest
Josh,
thanks, I was actually rephrasing / correcting my last post, and clarified a number of things to myself while doing that... I didn't think you could see my second post while I was editing it... sorry.
But it was not all in vain, as what you wrote last confirms what I was getting to while revising my question and in light of your previous answer, and things seem clear now.
Thanks much
@@stedev2256 bam! :)
One good idea for cross-validation maybe is we can split data first to train and test and again split train to train and validation sets. Therefore, we can guarantee that our test set is totally new to environment. This will result in more realistic scores.
yep
Was looking for this comment.
Thank you for these videos Josh, I really love learning from them. Just one question, when we do the cross validation, should not the alphas be different compared to those in the full sized training data and also on the different cross validation set? If yes, how should we decide which alpha should get the most vote as they are basically different on every training data?
To be honest, I don't know how it is implemented in practice, but I would guess that the alphas are in comparable ranges.
Perfect!
Thanks!
Best Explanation Dam !!!
Thanks! :)
I'm 50% here for stats and 50% here for the sound effects!
double bam! :)
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Thank you! I'm sooooo excited about the holiday. :)
You are the best.
Thank you, and thank you for supporting me! :)
There are some notifications... Right when it shows on ur phone... ur feelings says.. I going to learn something today...
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Hooray!!!! :)
Thank you very much
You got me at Smelly Stat!
bam!
At 12:20, let's say the right-hand side of the tree had node instead of just a leaf, and that node led to two leaves. In this situation, what would be pruned first to created the pruned comparison tree: prune the two leaves at the bottom of the left side only first because it's deeper? or the two leaves at the bottom of the right only first? or prune all ends with two leaves at the same time?
We always remove the leaves that result in the smallest increase in SSR.
Hey, Josh! Is it ok that we are using train+test to find alpha values? I mean that we are peeping into the future. do we know good thresholds using a test sample(not only train), or am I wrong? Thank you
You can set aside a set of data for final validation.
Dear Josh, thanks a lot for this video! It's awesome! You told us how to prune regression trees and your explanation was very clear. I've got a question, how can I prune classification trees? What is the biggest difference between regression trees and classification trees when pruning? I guess that the Tree Score is calculated in a different way when we prune classification trees. Can we simply add tree complexity penalty (alpha*number of leaves) to Gini Impurity to get a Tree Score in case of classification problem?
You pretty much add alpha to the total gini impurity for the tree. See: scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html
Thank you for the amazing content Josh!
I had a doubt, do we change the alpha and the number of nodes simultaneously cause I thought that we change just alpha and then check which tree performs better (keeping one thing constant and changing the other gives us a better way to compare). Also at 11:32, you used the entire data (train+test) to build the model and find different values of alpha and then used those values on train split to build the model, compare test scores and finally find the best value of alpha. If we use the train+test data to find alpha, isn't that causing data leakage?
Also, why do we compute the alpha values beforehand? why don't we do it the usual way(the way we do in ridge regression) where we find the optimal value of regularization parameter using GridSearchCV because anyhow alpha is analogous to regularization parameter (I think).
1) We find values for alpha to result in better performance after pruning.
2) We can keep some data set aside for validation.
3) Perhaps for performance reasons.
@@statquest sorry. I don't get your answer for #1. Can you please elaborate more? Why do you find alpha using test+training data? If you just use the training data to find alpha, you will get an alpha to result in a good performance, but it's better if we use both training and testing set. CAn you please confirm?
@@shawnkim6287 Presumably using more data results in better performance.
@@statquest Thank you. Can you please confirm what I understand from this video?
You pick a number of alphas from a set which combines both training and test set. First, you have the full (untouched) tree where alpha is 0. Then, you increase alpha until pruning leaves will give us a lower tree score. You increase alpha until pruning additional leaves will give us a lower tree score. Long story short, a set of alphas are derived from the (training and testing) sets.
You train the models using a set of alpha's that we calculated before.
We then test the trained models by picking the optimal alpha value from the alphas by selecting the lowest SSE in the first fold which is the testing set. You pick the next optimal alpha value by selecting the lowest SSE in the second fold as the next testing set. If you are using 5-fold CV, then you have to pick 5 optimal alpha's on 5 different testing set. You just select the best alpha that appear the most.
can you please verify if what I understand is correct? Thank you, Josh!
@@shawnkim6287 That seems reasonable to me. If you want to double check, you can find the algorithm in The Introduction to Statistical Learning in R (which is a free PDF if you look for it).
Well explained, thank you so much
Glad you liked it!
At 12:33, it reads on the video that "we increase alpha again until pruning leaves will give us a lower tree score". My question is lower than what? My understanding is lower than tree score of the full sized tree at that specific alpha value, in this example, at alpha=10000, because we have already established the tree score of the full sized tree at alpha = 0 has the smallest tree score. Similarly at 12:43, the third tree at alpha = 15,000 is chosen because it has lower tree score than the second tree at alpha = 15000. Please let me know if this is correct. Thanks.
Yes
Please publish a session on reduced error pruning also.
Very clear. Thank you very much
Thank you!
@Josh Starmer Will you please consider creating a quest for implementing the above explanation in R?
Will be really helpful !!!
P.S Great Quest :)
Working on it!
Good explanation in general, especially that this topic is difficult. But can you suggest where I could learn more about making post-pruning decision trees in R.
Unfortunately I only have a video shows these steps in Python: th-cam.com/video/q90UDEgYqeI/w-d-xo.html
you are literally doing god's work
Thank you!
A quick question please. In this example, the tree is not balanced, in the sense that right subtree is a lot deeper. What if the left subtree is as deep as the right subtree, then how do we choose which side of the internal node to collapse, or in other words, which side of the leaf nodes to delete? Based on what is shown at 12:33, we should go with whichever that gives us lower tree score, right?
Correct
This video is awesome! Why didn't I know StatQuest earlier, it really helps, THX!!!
btw: I'm confused about the built of trees in 13:19, how could we know that it is the bottom 2 leaves should be cut for a new training set when α=10000(pre-calculated), is that just a coincidence? Or the cut depends on which leaves give the lowest Tree Score?
My understanding of the pruning process is this:
①use the whole data set to build a full-sized tree, and then increase alpha from 0 to get different sub-trees that are pruned and have lower Tree Score corresponding to different alpha
②build a new training set, testing set from whole data, then build a full-sized tree, using the alpha we have before to build sub-trees, then calculating SSR on testing data
③repeat ② til we done 10-fold cross validation
④for each iteration, choose the alpha that has the lowest SSR
⑤calculate the average among all alpha in ④ to get its final value
Am I get it right？
You're correct about everything except 5. We don't calculate the average of the alphas, we calculate the average of the sum of the squared residuals for each level of alpha, and select the level of alpha that corresponds to the lowest average sum of the squared residuals.
@@statquest Thx so much for your reply and correction! I think I might get the process and concept a little bit, but still need more time to fully understand it, and your videos are just the most helpful, vividly, clearly! Thx again for all the efforts and sharing!
I'm moving to SVM right now hahaha
@@statquest and BAAAM!
@@statquest I am also having same doubt, can you please answer the original question? "how could we know that it is the bottom 2 leaves should be cut for a new training set when α=10000(pre-calculated), is that just a coincidence? Or the cut depends on which leaves give the lowest Tree Score?"
this guy is a living legend ❤
Thank you!
Just a random thought, what if we prune the tree directly based on ssr computed on validation set instead of adding penalty. Anyway the tree that works well on validation set is selected. Why are adding penalty?. Does it helps controlling fluctuations in the number of leafs selected across cross validation folds during Hypertuning .
How new trees are build by imposing previous alpha values? Maybe it is not possible to find new continuously smaller trees of reduced Tree Scores for fixed alphas. Before alpha was a parameter and during cross validation is a constraint :(
So a question. We learned previously that cross validation is used to test the model on different "blocks" of the test set. But in this case you are advocating for the cross validation to be used for hyper parameter tuning. Does that mean the test sets remain constant?
A lot of people ask about this and I could have probably done a much better job wording things. The way I see it, is that we have all of the data and we can split that in to "all the data we want for training" and "all the data we want for testing". We then build a tree and prune etc. using all the data we want for training and test against the testing data.
Hi Josh , I hope u answer my question, I was searching for 3 days till now and i got nothing
I have 2 problem which is :
1_ How to determine alpha where there is more one leaf in the bottom of tree (i.e : u said increase alpha till pruning this leaf get lower score) , so if i have more than one leaf in the last level of tree, which one should i cut or should i look for all subtrees every time increasing the alpha it seems like it will get high complexity?
2_ in implementation when i will give the model the ideal alpha to implement the decision tree, how the model will know when building it in every step he take is that will lead to the subtree related to this alpha
finally , u r such amazing i really enjoyed every lesson i took from this channel
1) You remove the leaf that results in the smallest increase in SSR.
2) You build the full tree, and prune just like we did before.
@@statquest
Thank u josh ,I really appreciate ur time for answering my questions
Hey Josh, Great video ! Quick questions: for classification tree, do we simply replace SSR with gini impurity and follow similar steps?
Yes.
Thanks for the great video. One question though: Why is the full-sized tree build from all data (see 11:25) and not just the testing data? Couldn't this potentially give problems w.r.t. leakage?
You can always create a validation dataset and hold onto that until the end.
I thought as much but I was a bit astonished that it was emphasized to use all data. Thanks for the clarification.
Hey Josh, how can we choose our alpha wisely? So that the tree with the minimum tree score will really work well for testing data too. Is there a specific rule of thumb?
I give a practical tutorial on building trees with real data here: th-cam.com/video/q90UDEgYqeI/w-d-xo.html
Thank Josh, for your lucid explanation. We are longing for the XGBoost Videos. Any updates on that ?
This was actually the very first XGBoost video. XGBoost uses unique trees and to understand why, you have to know everything about normal regression trees. This information was originally in a XGBoost StatQuest, but it was too much, so I made it a stand alone video. That being said, the next video I put out, in the next two weeks or so, will be on XGBoost, then the next and the next, etc. XGBoost is a huge algorithm with a lot of parts. I've got 3 videos worth of material so far and I've only scratched the surface. I'm expecting to have at least 4 XGBoost videos, maybe more.
@@statquest Thanks so much for the reply. I've seen all your tree based videos. For the last two days, I've been reading about XGBoost all over the internet and it was quiet difficult to grasp the whole picture of XGB. I genuinely thought it would be lot more easier and intuitive if it had been explained by you. Appreciate all your work. Couldn't be more excited for the future XGBoost videos. BAM!
@@mohamedhanifansari9224 Just a few more weeks to wait! (and I'm just as excited as you are about this XGBoost thing - it's become an obsession!)
@@statquest Thanks so much! :')
What about order of removing leaves? If we have a huge tree, do we need to generate all possible combinations of subtrees and alpha values?
If you know what the sum of the squared errors are for each node in the tree, you can systematically remove leaves/splits for a specific value for alpha to work without having to generate all possible trees
Regarding choosing α (starting at 11:19) when we fit a new regression to the FULL data, does this not cause us to 'overfit' α to some extent? I was wondering what would happen if we did the following:
i) Split the data k different ways into a set that we find α for and a set that we ignore.
ii) On the set that we find alpha for, we get [α11, α12, α13] (the first set of α's such that we get better tree scores cutting the tree by 1, 2, and 3 levels respectively.) up to [αk1, αk2, αk3].
iii) We then take the average α for each cut so [(α11+α21+...+αk1) / k, (α12+α22+...+αk2) / k, (α13+α23+...+αk3) / k] as our set of final α's.
iv) Perform K-Fold Cross Validation using the above to see what α gives the lowest SSR for it's optimal tree.
Would my method make little to no difference? Or is my method overfitting more in some sense? Let me know what you think!
It seems reasonable to me.
Cost Complexity Pruning is otherwise known as post-pruning, while limiting the tree depth, enforcing a minimum amount of samples per leaf/split, a minimum impurity decrease is known as pre-pruning, correct? My question is, can you apply pre-pruning first, and then apply post-pruning to the pre-pruned tree? If yes, then i assume that the alpha parameters will be found from the pre-pruned tree, right? Not the initial full tree? And then cross validation will also be performed with the pre-pruned tree, not the initial full tree, to determine the final optimal alpha score?
And on a separate note, should only those alpha obtained from the tree trained on all the data be used when cross validating, and why? Is there no chance that some other, random alpha, might result in better performance? Considering that cross validation is done on several different test/train splits, and there will be those that do better with one alpha, and those that do better with other alphas, doesn't it make sense to try all possible alphas (from 0 to infinity) in the cross validation, not only those that give the best tree scores for the full tree? Isn't there a chance that some other alpha will give, on average, a lower sum of squared residuals than those obtained from the full tree?
I believe you are correct about how pre-pruned trees are used. And this is just how the algorithm is spelled out - possibly to keep the running time down.
Thanks for the great video, Josh. Quick question. At 15:06 what is the exact meaning of "on average"? Do you mean simply the most frequently selected alpha during the CV is the final value? Or, should I have to calculate the average of SSR for test set, something like that?
It means that the same alpha value may not always give you the best tree during each fold of cross validation, but most of the time it does.
@@statquest Thank you for the quick reply and sorry for the unclear question. What I wanted to ask is the way of averaging. Should I count how many times each alpha was the best during each fold of C.V.? And then pick the most frequently selected alpha?
@@onyman8837 yes
Hello,
This is a clearly explained video and thanks for this entire series.
Can you please let us know how to implement the pruning using sklearn in python?
That's a good idea! I'll put it on the to-do list.
Josp. Great job as usual however you just pulled the starting alpha and subsequent values out of the air. How did you come up with these values?
At 8:22 I explain where we get alpha. We use cross validation. That means we try a bunch of values and test each one to find out which is best.
@@statquest ok I’ll take another look. Thanks.
@StatQuest with Josh Starmer, I have purchased your book but I didn't find these concepts (pruning, random forest, adaboost, gradient boosting) in that. Is there a way to access these presentation slides?
Those will be in a future book.
i'm curious as to how we find sensible alphas. there seems to be an explanatory gap here since in the section "comparing pruned trees with alpha" it says (in the NOTE at 8:23) that we find it during cross validation, but in the cross validation section at 13:18 we are supposed to "use the a values we found before". sensible alpha values would probably vary widely depending on the SSRs of the trees (and ultimately the variable ranges) and even more so once we do classification since gini and entropy give small values for which alphas of 10k would not work usefully. surely simply guessing various alpha levels and finding which of the guessed ones work best in cross validation is not the best method or am i misunderstanding something here?
We use all of the data to find candidate values for alpha (this is demonstrated at 11:18). Once we have candidate values, we test each one with cross validation to find the optimal value for alpha.
Friends reference - cherry on top! :)
Hooray!!! You got it! :)
Great explanation. I really enjoyed the video, but I'm a bit confused. Why use all of the data to build the initial tree in step 1? If we do that there is no test data left for testing the tree.
This is assuming you've already separated out your testing and validation data.
Hi Josh, Thanks for this GREAT video!!! Just wanted to ask what's the principal to choose several alpha as the starting values? ie. in the video, you choose these 3 alpha values as the candidates to determine the final alpha: 10,000, 15,000, 22,000, how did you come up with these three alpha?
I started with alpha =0. I then increased alpha until I got a lower SSR + alpha score by pruning a branch. That was my second value for alpha. I then increased until I got a lower SSR + alpha by pruning another branch...etc.
@@statquest Thanks! Make perfect sense to me. Hooray! : )
@@statquest Thank you Josh! Really nice videos! I am looking at most of them! I rank them by the total BAM count of each video :)
One thing I don't understand is: in the example above the full tree and the tree with one pruned leaf would have an equal tree score at alpha = 5494.8 - 543.8 = 4951.0, whereas the trees with one and two pruned nodes would have an equal score at 19243.7 - 5494.8 = 13748.9. So instead of choosing a candidate alpha of 10000 as you did in the very first step, you could have chosen any value 4951.0 < alpha < 13748.9.
Would perhaps the best value to choose for a candidate alpha then be the mean of 4951.0 and 13748.9 = 18699.9 rather than 10000?
@@fredrikedin8880 What time point, minutes and seconds, are you asking about?
@@statquest Sorry for my unclear question. Between 12:00 and 12:54 you describe that you increase alpha until pruning one leaf renders a lower score. So the border between the full tree and the tree with one pruned leaf is at 10000 here, and the border between the trees with one and two pruned leaves is at 15000. Then couldn't the candidate alpha for the tree with one pruned leaf be set to 12500, i.e. the middle of the interval where this subtree is optimal, rather than at the border to the full tree?