Corrections: 16:50 I say "66", but I meant to say "62.48". However, either way, the conclusion is the same. 22:03 In the original XGBoost documents they use the epsilon symbol to refer to the learning rate, but in the actual implementation, this is controlled via the "eta" parameter. So, I guess to be consistent with the original documentation, I made the same mistake! :) Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/
@blacklistnr14 жыл бұрын
Terminology alert!! "eta" refers to the greek letter Η(upper case)/η(lower case), it is one of the greek's many "ee" sounds(as in wheeeeee), it's definitely not epsilon.
@MrPopikeyshen3 жыл бұрын
like just for this sound 'bip-bip-pilulipup'
@servaastilkin7733 Жыл бұрын
@@blacklistnr1 I came here to say the same thing. Maybe this helps: èta - η sounds somewhat like the vowels in "air" epsilon - ε sounds somewhat like the vowel in "get"
@pulkitkapoor40913 жыл бұрын
I got my first job in Data Science because of the content you prepare and share. Can't thank you enough Josh. God bless :)
@statquest3 жыл бұрын
That is awesome! Congratulations! TRIPLE BAM! :)
@SaurabhMishra-tt5qt2 жыл бұрын
which company bro?
@sendhana-46 Жыл бұрын
kya company bhai?
@ImGeneralJAckson5 ай бұрын
Same :-)
@giannislazaridis67884 жыл бұрын
I'm starting writing my Master Thesis and there were still some things I needed to make clear before using XGBoost for my classification problem. God Bless You
@statquest4 жыл бұрын
Thank you! :)
@Hardson4 жыл бұрын
That's why I pay my Internet.
@statquest4 жыл бұрын
Thanks! :)
@nikilisacrow23393 жыл бұрын
Can I just say I LOVE STATQUEST! Josh does the intuition of a complex algorithm and the math of it so well and then to make it into an engaging video that is so easy to watch is just amazing! I just LOVE this channel. You you boosted the gradient of my learning on machine learning in an extreme way. Really appreciate these videos
@statquest3 жыл бұрын
Wow! Thank you very much!!! I'm so glad you like the videos. :)
@johnhutton5491Ай бұрын
This dude puts the STAR in starmer. You are an international treasure.
@statquestАй бұрын
Thank you! :)
@guoshenli41933 жыл бұрын
I am a graduate student at Duke, since some of the materials are not covered in the class, I always watch your videos to boost my knowledge. Your videos help me a lot in learning the concepts of these tree models!! Great thanks to you!!!!! You make a lot of great videos and contribute a lot in online learning!!!!
@statquest3 жыл бұрын
Thank you very much and good luck with your studies! :)
@ChingFungChan-b4l12 сағат бұрын
Hi Josh, I just bought your illustrated guide in PDF. This is the first time I've supported someone on social media. Your video helped me a lot with my learning. Can't express how grateful I'm with these learning materials. You broke down monster maths concepts and equation to baby monster that I can easily digest. I hope by making this purchase, you get the most contribution out of my support. Thank you!
@statquest7 сағат бұрын
Thank you very much for supporting StatQuest! It means a lot to me that you care enough to contribute. BAM! :)
@DonDon-gs4nm4 жыл бұрын
After watching your video, I understood the concept of 'understanding'.
@andreitolkachev82953 жыл бұрын
I wanted to watch this video last week, but you sent me on a magical journey through adaboost, logistic regression, logs, trees, forests, gradient boosting.... Good to be back
@statquest3 жыл бұрын
Glad you finally made it back!
@pranavjain9799 Жыл бұрын
same haha
@kamalamarepalli11654 ай бұрын
I have never seen an data science video like this....good informative, very clear, super explanation of math and wonderful animation and energetic voice....Learning many things very easily....thank you so much!!
@statquest4 ай бұрын
Thank you very much!
@moidhassan55523 жыл бұрын
Wow, I am really interested in Bioinformatics and was learning Machine Learning techniques to apply to my problems and out of curiosity, I checked your LinkedIn profile and turns out you are a Bioinformatician too. Cheers
@statquest3 жыл бұрын
Bam! :)
@pavankumar69924 жыл бұрын
Fantastic explanation for XGBoost. Josh Starmer, you are the best. Looking forward to your Neural Network tutorials.
@statquest4 жыл бұрын
Thanks! I hope to get to Neural Networks as soon as I finish this series on XGBoost (which will have at least 3 more videos).
@nitinvijayy2 жыл бұрын
Best Channel for anyone Working in the Domain of Data Science and Machine Learning.
@statquest2 жыл бұрын
Thanks!
@RidWalker9 ай бұрын
I've never I had so much fun learning something new! Not since I stared at my living room wall for 20min and realized it wasn't pearl, but eggshell white! Thanks for this!
@statquest9 ай бұрын
Glad you got the wall color sorted out! Bam! :)
@breopardo66913 жыл бұрын
In my heart, there is a place for you! Thank you Josh!
@statquest3 жыл бұрын
Thanks!
@PauloBuchsbaum4 жыл бұрын
An incredible job of clear, concise and non-pedantic explanation. Absolutely brilliant!
@statquest4 жыл бұрын
Thank you very much!
@prasanshasatpathy66642 жыл бұрын
Nowadays I write a "bam note" for important notes for algorithms.
@statquest2 жыл бұрын
That's awesome! :)
@mainhashimh50172 жыл бұрын
Man, the quality and passion put into this. As well as the sound effects! I'm laughing as much as I'm learning. DAAANG. You're the f'ing best!
@statquest2 жыл бұрын
Thank you very much! :)
@shhdeshp7 ай бұрын
I just LOVE your channel! Such a joy to learn some complex concepts. Also, I've been trying to find videos that explain XGBoost under the hood in detail and this is the best explanation I've come across. Thank you so much for the videos and also boosting them with an X factor of fun!
@statquest7 ай бұрын
Awesome, thank you!
@tusharsub10003 жыл бұрын
I had left all hope of learning machine learning owing to its complexity. But because of you I am still giving it a shot..and so far I am enjoying...
@statquest3 жыл бұрын
Hooray!
@gawdman4 жыл бұрын
Hey Josh! This is fantastic. As an aspiring data scientist with a couple of job interviews coming up, this really helped!
@statquest4 жыл бұрын
Awesome!!! Good luck with your interviews and let me know how they go. :)
@jaikishank3 жыл бұрын
Thanks Josh for your explanation. XGBoost explanation cannot be made simpler and illustrative than this. I love your videos.
@statquest3 жыл бұрын
Thank you very much! :)
@glowish19934 жыл бұрын
You make learning math and machine learning interesting and allow viewers to understand the essential points behind complicated algorithms, thank you for this amazing channel :)
@statquest4 жыл бұрын
Thank you! :)
@SaraSilva-zu7wn2 жыл бұрын
Clear explanations, little songs and a bit of silliness. Please keep them all, they're your trademark. :-)
@statquest2 жыл бұрын
Thank you! BAM! :)
@hanyang43213 жыл бұрын
I watched all of the videos in your channel and they're extremely awesome! Now I have much deeper understanding in many algorithms. Thanks for your excellent work and I'm looking forward to more lovely videos and your sweet songs!
@statquest3 жыл бұрын
Thank you very much! :)
@kennywang99294 жыл бұрын
Man, you do deserve all the thanks from the comments! Waiting for part2! Happy new year!
@statquest4 жыл бұрын
Thanks!!! I just recorded Part 2 yesterday, so it should be out soon.
@hellochii16754 жыл бұрын
xgboosting!This must be my Christmas 🎁 ~~ Happy holidays ~
@statquest4 жыл бұрын
Yes, this is sort of an early christmas present. :)
@jjlian16704 жыл бұрын
I have been waiting for your video for XGBoost, hope for LightGBM next!
@anupriy2 жыл бұрын
Thanks for making such great videos, sir! You indeed get each concepts CLEARLY EXPLAINED.
@statquest2 жыл бұрын
Thank you! :)
@jackytsui4224 жыл бұрын
I am learning machine learning from scratch and your videos helped me a lot. Thank you very much!!!!!!!!!!!
@statquest4 жыл бұрын
Good luck! :)
@machi9923 жыл бұрын
I actually started looking for XGBoost, but every video assumes I know something. I have ended up watching more than 8 videos just to have no problems understanding and fulfilling the requirements, and find them awesome.
@statquest3 жыл бұрын
Bam! Congratulations!
@Azureandfabricmastery4 жыл бұрын
Thank you! Super easy to understand one of the important ml algorithm XGBoost. Visual illustrations are the best part!
@statquest4 жыл бұрын
Thank you very much! :)
@karannchew25343 жыл бұрын
For my future reference. 1) Initiate with a predicted value e.g. 0.5. 2) Get residual. Each sample vs. initial predicted value. 3) Build a mini tree, using the Residuals value of each sample. .Residuals .Different values of feature as cut off point at branches. Each value give a set of Similarity and Gain scores ..Similarity (use lambda here, the regularisation parameter) - measure how close the residual values to each other ..Gain (affected by lamda) .Pick the feature value that give highest gain - this determines how to split the data - which create the branch (and leaves) - which produce a mini tree. 4) Prune tree. Using gain threshold (aka complexity parameter), gamma. If gain>gamma, keep branch, else prune 5) Get Output Value OV for each leaf. Mini tree done. OV = sum of Residuals / (no. of Residuals + lambda) 6) Predict value for each sample using the newly created mini tree. Run each sample data through the mini tree. New Predicted value = last predicted value + eta * OV 7) Get new set of residual: New predicted value vs actual value of each sample. 8) Re do from step 3. Create more mini trees... .Each tree 'boosts' the prediction - improving the result. .Each tree creates new residual as input to creating the next new tree. ...until no more improvement or no. of tree is reached.
@statquest3 жыл бұрын
Noted
@carlpiaf4476 Жыл бұрын
Could be improved by adding how the decision cut off point is made.
@modandtheganggaming36174 жыл бұрын
Thank you! I'd been waited for XGBoost explained for so long
@statquest4 жыл бұрын
I'm recording part 2 today (or tomorrow) and it will be available for early access on Monday (and for everyone a week from monday).
@guillemperdigooliveras53514 жыл бұрын
As always, loved it! I can now wear my Double Bam t-shirt even more proudly :-)
@statquest4 жыл бұрын
Awesome!!!!!! :)
@anggipermanaharianja61223 жыл бұрын
why not wearing the Triple Bam?
@guillemperdigooliveras53513 жыл бұрын
@@anggipermanaharianja6122 for a second you gave me hopes about new Statquest t-shirts being available with a Triple Bam drawing!
@mangli46694 жыл бұрын
Hey Josh, first I wanted to say thank you for your awesome content. You are the number one reason I am graduating my degree haha! I would love a behind the scenes video about how you make your videos. How you prepare for topic, how you make your animations and your fancy graphs! And some more singing ofcourse!
@statquest4 жыл бұрын
That would be awesome. Maybe I'll do something like this in 2020. :)
@mentordedados2 жыл бұрын
You are the best, Josh. Greetings from Brazil! We are looking forward you video explaining clearly the LightGBM!
@statquest2 жыл бұрын
I hope do have that video soon.
@nilanjana1588 Жыл бұрын
You make it little bit easy to understand Josh . I am saved.
@statquest Жыл бұрын
Thanks!
@gorilaz0n2 жыл бұрын
Gosh! I love your fellow-kids vibe!
@statquest2 жыл бұрын
Thanks!
@liuxu78792 жыл бұрын
Hey Josh, I really love your contents, you are the one who really explains the model details.
@statquest2 жыл бұрын
WOW! Thank you so much for supporting StatQuest!
@oldguydoesntmatter28724 жыл бұрын
I've been using Random Forests with various boosting techniques for a few years. My regression (not classification) database has 500,000 - 5,000,000 data points with 50-150 variables, many of them highly correlated with some of the others. I like to "brag" that I can overfit anything. That, of course, is a problem, but I've found a tweak that is simple and fast that I haven't seen elsewhere. The basic idea is that when selecting a split point, pick a small number of data vectors randomly from the training set. Pick the variable(s) to split on randomly. (Variables plural because I usually split on 2-4 variables into 2^^n boosting regions - another useful tweak.) The thresholds are whatever the data values are for the selected vectors. Find the vector with the best "gain" and split with that. I typically use 5 - 100 tries per split and a learning rate of .5 or so. It's fast and mitigates the overfitting problem. Just thought someone might be interested...
@zhonghengzhang6034 жыл бұрын
Sounds awesome, would you like share the code?
@vladimirmihajlovic15044 ай бұрын
Love StatQuest. Please cover lightGBM and CatBoost!
@statquest4 ай бұрын
I've got catboost, you can find it here: statquest.org/video-index/
@shubhambhatia49684 жыл бұрын
woah woah woah woah!... now i got the clear meaning of understanding after coming to your channel...as always i loved the xgboost series as well. thank you brother.;)
@statquest4 жыл бұрын
Thank you very much! :)
@monkeydrushi Жыл бұрын
God, thank you for your "beep boop" sounds. They just made my day!
@statquest Жыл бұрын
Hooray! :)
@nickbohl25554 жыл бұрын
I have been super excited for this quest! Thanks as always Josh
@statquest4 жыл бұрын
Hooray!!!!
@anggipermanaharianja61223 жыл бұрын
Awesome... this vid should be a mandatory in any schools
@statquest3 жыл бұрын
bam! :)
@smarttradzt49332 жыл бұрын
whenever i can't understand anything, I always think of statquest...BAM!
@statquest2 жыл бұрын
bam!
@user-jx7ft7ir7d4 жыл бұрын
Awesome video!!! It's the best tutorial I have ever seen about XGBoost. Thank you very much!
@statquest4 жыл бұрын
Thank you! :)
@palvinderbhatia394110 ай бұрын
Wow woww wowww !! How can you explain such complex concepts so easily. I wish I can learn this art from you. Big Fan!! 🙌🙌
@statquest10 ай бұрын
Thank you so much 😀
@geminicify4 жыл бұрын
Thank you for posting this! I have been waiting for it for long!
@statquest4 жыл бұрын
Hooray! :)
@gokulprakash86943 жыл бұрын
Stat quest is the bestttttt!!! love it love it love it!!!!!!
@statquest3 жыл бұрын
Thank you! :)
@0xZarathustra4 жыл бұрын
pro tip: speed to 1.5x
@shivasaib90233 жыл бұрын
I fell in love with XGBOOST. While Pruning every node I was like whatttt :p
@statquest3 жыл бұрын
:)
@lxk199014 жыл бұрын
This is really helpful, thanks for putting them together!
@statquest4 жыл бұрын
Thank you! :)
@ramnareshraghuwanshi5163 жыл бұрын
Thanks for uploading this.. i am your biggest fan!! I have noticed too many adds these days which really disturb :)
@statquest3 жыл бұрын
Sorry about the adds. KZfaq does that and I can not control it.
@urvishfree03143 жыл бұрын
thankyou so much i watched it 3-4 times already but finally everything makes sense. thankyou so much
@statquest3 жыл бұрын
Hooray!
@junaidbutt30004 жыл бұрын
This has been one video I’ve been waiting for and it was well worth it. Brilliant as usual Josh. I wanted to ask about the differences between the XGBoost regression tree and the traditional regression tree with Boosting. It seems that the main difference is that the XGBoost version uses the gain measure (made of similarity) to determine the split thresholds for each feature (I presume if we had more than dosage we would consider them in the same way) and prunes according to the gamma parameter. Whereas the traditional tree uses a measure like Gini impurity to split and a method like cost complexity pruning. Is that the main difference? Or are there any more? Could you also mention why this type of tree is better than the traditional version? It seems like the algorithm has some optimisation for this type of tree than the other.
@statquest4 жыл бұрын
There are lots of differences, however, the fundamental difference in trees is a big one. I believe the reason for XGBoost trees is that the computation can be easily optimized compared to traditional regression trees. The other major differences are optimizations for very large datasets - XGBoost was one of the first machine learning algorithms developed specifically for "big data" so it has tricks for working with datasets that can't all fit into memory. I'll talk about these in Part 4 (part is how XGBoost trees work for classification and part 3 derives the math and theory that underlies XGBoost trees.).
@sidbhatia42304 жыл бұрын
Thanks, it helped a lot! Looking forward to part 2, and if possible please make one on catboost as well!
@HANTAIKEJU3 жыл бұрын
Hi Josh, Love your videos. Currently preparing Data Science interviews based on your video. Actually, really want to hear one about LGBM !
@statquest3 жыл бұрын
I'll keep that in mind.
@omkarjadhav134 жыл бұрын
You just amazing Josh. Xtreme Bam!!! You make our life so easy. Waiting for neural net vid and further Xgboost parts. Please plan a meetup in Mumbai. #queston
@statquest4 жыл бұрын
Thanks so much!!! I hope to visit Mumbai in the next year.
@ksrajavel4 жыл бұрын
@@statquest Happy New Year, Mr. Josh. New year arrived. Awaiting you in India.
@statquest4 жыл бұрын
@@ksrajavel Thank you! Happy New Year!
@fivehuang75574 жыл бұрын
Happy holiday man! Waiting for your next episode
@statquest4 жыл бұрын
It should be out in the first week in 2020.
@kn586574 жыл бұрын
I'm doing a club remix of the humming during calculations. Stay tuned!
@statquest4 жыл бұрын
Awesome!!!!! I can't wait to hear.
@natashadavina75923 жыл бұрын
your videos have helped me a lot!! thank you so much i hope you keep on making these videos:)
@statquest3 жыл бұрын
Thanks!
@sajjadabdulmalik42653 жыл бұрын
You are always awesome no better explanation ever seen like this ❤️❤️ big fan 🙂🙂.. Triple bammm!!! Hope we have Lightgbm coming soon.
@statquest3 жыл бұрын
I've recently posted some notes on LightGBM on my twitter account. I hope to convert them into a video soon.
@eytansuchard8640 Жыл бұрын
Thank you for this explanation. In python there is another regularization parameter, Alpha. Also, to the best of my knowledge the role of Eta is to reduce the error correction by subsequent trees in order to avoid sum explosion and in order to control the residual error correction by each tree.
@statquest Жыл бұрын
I believe that alpha controls the depth of the tree.
@eytansuchard8640 Жыл бұрын
@@statquest The maximal depth is a different parameter. Maybe Alpha regulates how often the depth can grow if it did not reach the maximal depth.
@statquest Жыл бұрын
@@eytansuchard8640 Ah, I should have been more clear - I believe alpha controls pruning. At least, that's what it does here: kzfaq.info/get/bejne/epaVmat2r9nKeKM.html
@eytansuchard8640 Жыл бұрын
@@statquest Thanks for the link. It will be watched.
@andrewnguyen58814 жыл бұрын
Thank you for all of your videos! Super helpful and educational. I did have some questions for follow-up: - With Gamma being so important in the pruning process, how do you select gamma? I ask because aren't there situations where you could select a Gamma that would/wouldn't prune ALL branches, which would defeat the purpose of pruning right? - Is lambda a parameter that: a. Have to test multiple and tune your model to find the most suitable lambda (ie set your model to use one lambda) b. You test multiple lambdas per tree so different trees will have different lambdas
@statquest4 жыл бұрын
If you want to know all about using XGBoost in practice, see: kzfaq.info/get/bejne/fdh6g5x3sbyXdnk.html
@andrewnguyen58814 жыл бұрын
@@statquest Great! I was saving that video until i finished the other XGBoost videos
@andrewnguyen58814 жыл бұрын
@@statquest Will this video also cover Cover from the Classification video?
@statquest4 жыл бұрын
Not directly, since I simply limited the size of the trees rather than worry too much about the minimum number of observations per leaf.
@praveerparmar81573 жыл бұрын
That DANG was unexpected.....you should have given a DANG alert 😋
@statquest3 жыл бұрын
:)
@tobiasksr232 жыл бұрын
I justo found this channel and i think it's amazing.
@statquest2 жыл бұрын
Glad to hear it!
@alfatmiuzma Жыл бұрын
Can't thank you enough, MGB you 😊😊😊
@statquest Жыл бұрын
Thanks!
@oriol-borismonjofarre6114 Жыл бұрын
Josh you are amazing!
@statquest Жыл бұрын
Thank you!
@vijayyarabolu90673 жыл бұрын
8:45 checking my headphones - BAM; no problem with my headphones; 10:17 Double BAM; headphones are perfect
@statquest3 жыл бұрын
:)
@iop09x094 жыл бұрын
Wow! Very well explained, hats off.
@statquest4 жыл бұрын
Thanks! :)
@metiseh3 жыл бұрын
Bam!!! I am totally hypnotized
@statquest3 жыл бұрын
Thanks!
@adityanimje8433 жыл бұрын
Hey Josh, love your videos :) Any idea when you will make the videos for CatBoost and Light GBM ?
@statquest3 жыл бұрын
Maybe as early as July.
@adityanimje8433 жыл бұрын
@@statquest Thank you :) One more question - I was reading Light GBM documentationand it said Light GBM grows "leaf wise" where as most DT algorithm grow "level wise" and that is a major advantage of Light GBM. But in your videos ( RF and other DT algortihm ones ), all of the videos show that they are grown "leaf wise". Am I missing miunderstanding something here ?
@statquest3 жыл бұрын
@@adityanimje843 I won't know the answer to that until I start researching Light GBM in July
@adityanimje8433 жыл бұрын
@@statquest Sure - thank you for the swift reply. Looking forward to your new videos in July :)
@bernardmontgomery38594 жыл бұрын
xgboosting! my Christmas gift!
@statquest4 жыл бұрын
Hooray! :)
@burstingsanta27103 жыл бұрын
that DANG!!! just brought my attention back😂
@statquest3 жыл бұрын
bam! :)
@terryliu36353 ай бұрын
Thank you, Josh., I'm watching your videos every day these past couple of months. Quick question, you mentioned the initial prediction being 0.5. Some other materials I read uses "the average of all the target values in the dataset"...could you pls help me understand this a little bit more about this initial prediction? Does it really matter between 0.5 and the mean of the target values?
@statquest3 ай бұрын
It is possible that XGBoost has since been updated to use the mean. However, when it was first released and described in manuscripts, it used 0.5.
@whenmathsmeetcoding18364 жыл бұрын
Gain in Similarity score for the nodes can be considered weighted reduction of variance of the nodes BTW good attempt to make this digestible to all
@statquest4 жыл бұрын
Thanks!
@DrJohnnyStalker4 жыл бұрын
Best XGBoost explanation i have ever seen! This is Andrew Ng Level!
@statquest4 жыл бұрын
Thank you very much! I just released part 4 in this series, so make sure you check them all out. :)
@DrJohnnyStalker4 жыл бұрын
@@statquest I have binge watched them all. All are great and by far the best intuative explanation videos on XGBoost. A series on lightgbm and catboost would complete the pack of gradient boosting algorithms. Thx for this great channel.
@statquest4 жыл бұрын
@@DrJohnnyStalker Thanks! :)
@pradeeptripathi13784 жыл бұрын
Hi.... I have 4 questions: 1) Are Gradient boosting and XGboost algorithm works in same way? Are they both using same steps like- Initial prediction, residual calculation, construct a first tree for fitting residuals etc OR there is a difference in steps (Note: I am not asking how Gradient boosting regression trees and XGBoost regreesion trees are created)? 2) Are initial prediction 0.5 is some random value? Can't we intialize initial value in a same way as we did in Gradient boosting (mean of Dependent variable)? 3) How have you decided threshold for dosage like Dosage15, and Dosage>22.5? Is there any steps or rule for that? 4) What learning parameter do and it should have small or high value? What is lamda here? Can we take any value of lambda? Sorry for many questions but answers of these queries will help me to learn better XGBoost. Thanks in advance
@statquest4 жыл бұрын
1) Both methods use the same steps. To learn more about Gradient Boost, see: kzfaq.info/get/bejne/aalzZ7Fl35mrepc.html 2) You can set the initial prediction to any value you want. I mention this at 2:43 3) Thresholds are decided just like for Regression Trees (only we use XGBoost Trees): kzfaq.info/get/bejne/nZ-TaZmFut_Qimg.html 4) The learning parameters and lambda provide regularization. To learn more about regularization, see: kzfaq.info/get/bejne/h55hhbVk3rHSY2Q.html
@pradeeptripathi73664 жыл бұрын
@@statquest My Understanding is: 1) Both algorithms uses the same steps to build a model except the steps for creating Gradient Boosting regression tree and XGBoost tree is different. Correct? 3) Thresholds for Regression Trees: Algorithm randomly try different threshold value at a node and see which threshold value has low Gini index or high information gain for that node. So, threshold value which has low gini index or high information gain for that node will be final threshold value and this will be done for each node. Threshold value for each nodes of XGBoost tree is also decided in same way?
@statquest4 жыл бұрын
@@pradeeptripathi7366 1) Both methods use the same steps. To learn more about Gradient Boost, see: kzfaq.info/get/bejne/aalzZ7Fl35mrepc.html 3) All tree algorithms use a systematic approach to testing all possible thresholds. Different tree algorithms use different criteria for deciding which threshold is optimal. Please watch my video on Regression Trees for more details: kzfaq.info/get/bejne/nZ-TaZmFut_Qimg.html
@ashfaqueazad38974 жыл бұрын
Life saver. Was waiting for this.
@vithaln76464 жыл бұрын
JOSH is the top data scientist in the world
@statquest4 жыл бұрын
Ha! Thank you very much! :)
@ayenewyihune2 жыл бұрын
I'm enjoying your videos. I'd love if you can do one on Tabnet.
@statquest2 жыл бұрын
I'll keep that in mind!
@yulinliu8504 жыл бұрын
Great Xmas present! Thanks Josh!
@statquest4 жыл бұрын
Hooray! :)
@sachinrathi78144 жыл бұрын
Waiting for this video since long back.
@statquest4 жыл бұрын
I hope it was worth the wait! :)
@sachinrathi78144 жыл бұрын
@@statquest Indeed. I have gone through many post but everyone is telling about it combine week classified to make strong classifier..n same description every. & Then the way of describing the things make differ Josh Starmer to others. Marry Christmas 🤗
@ahmedelhamy18453 жыл бұрын
Wonderful as usual Josh
@statquest3 жыл бұрын
Thanks!
@aldo6052 жыл бұрын
Thank you so much. You are the best
@statquest2 жыл бұрын
Thank you very much for supporting StatQuest! BAM! :)
@kandiahchandrakumaran85217 ай бұрын
Wonderful tutorials, not only this video, but every video in StatQuest. Probably, the best videos with good explanation available in KZfaq. I was stuggling with Python until I followed your videos. Now I am very confident in analysing the big data. One question: I am looking at the recurrence of a disease following surgery and I evaluate time for recurrence and probability with CPH. But in non-life science, eg. customer churn, default of payment etc there is no censored cases and not considered in the ML models, such as XGBoost. Is it correct for me to use ML in the similar way for cutomer churn and customer default, despite the censored data? Please advice. Many thanks. I (not only me every budding data scienist) would very much appreciate if you could create a tutorial video (and upload) generating Nomogram for Time event? This will help me (and others) to analyse and publish to a peer reviewed Journal on the dataset I've collected on Cancer recurrence. Best wishes.👍
@statquest7 ай бұрын
Thank you! To be honest, I don't really know the details of how to work with censored data with ML. My advice is to simply try it out (leave the censored data as missing) and see how it does. And I'll keep those topics in mind.
@scotthalpern563110 ай бұрын
This is fantastic!
@statquest10 ай бұрын
Thanks!
@iBenutzername Жыл бұрын
Hey Josh, the series is fantastic! I'd like to ask you to consider two more aspects of tree-based methods: 1) SHAP values (e.g., feature importance, interactions) and 2) nested data (e.g., daily measurements --> nested sampling?). I am more than happy to pay for that :-) thanks!
@statquest Жыл бұрын
I'm working on SHAP already and I'll keep the other topic in mind.
@iBenutzername Жыл бұрын
@@statquest That's great news, can't wait to see it in my sub box! Thanks a lot!
@tc3224 жыл бұрын
Xtreme Christmas gift!! :) Thanks!!
@statquest4 жыл бұрын
:)
4 жыл бұрын
Thank you for sharing this amazing video!
@statquest4 жыл бұрын
Thank you! :)
@oldguydoesntmatter28724 жыл бұрын
Bravo! Excellent presentation. I've been through it a bunch of times trying to write my own code for my own specialized application. There's a lot of detail and nuance buried in a really short presentation (that's a compliment - congratulations!). Since you have nothing else to do (ha! ha!), would you consider writing a "StatQuest" book? I'll bid high for the first autographed copy!
@statquest4 жыл бұрын
Thank you very much!
@parthsarthijoshi63013 жыл бұрын
XTREME BAM
@statquest3 жыл бұрын
YES! :)
@stylianosiordanis93624 жыл бұрын
please post slides, this is the best channel for ML. thank you
@aksaks23384 жыл бұрын
Hey Josh! Thanks for the video, just wanted to know when will you release part 2 and 3 of this?
@statquest4 жыл бұрын
Part 2 is already available for people with early access (i.e. channel members and patreon supporters). Part 3 will be available for early access in two weeks. I usually release videos to everyone 1 or 2 weeks after early access.
@louisa123 Жыл бұрын
Hi Josh, I have a question, in min 1 you blend in many phrases on the left side such as Approximate Greedy Algorithm, Weighted Quantile Sketch etc and you mention you will go through them one by one, but in this video only the first three phrases are covered. Are there videos on the other ones? I also wanted to thank you for all the work you put into your videos! Really helps me to understand complex concepts
@statquest Жыл бұрын
This is just the first video in a 4 part series. You can see the others here: kzfaq.info/get/bejne/hdp0a9qHxqzRZnk.html and all of my videos are organized here: statquest.org/video-index/
@gutsa33892 жыл бұрын
Amazing explanation as usual !!! Josh, is it possible to make a StatQuest about LightGBM ? I'm sure that it will help a lot of students like me. Thank you very much !
@statquest2 жыл бұрын
I am working on that one.
@gutsa33892 жыл бұрын
@@statquest Great !! We're waiting for that one. Thanks a lot