XGBoost: How it works, with an example.

In this excerpt, we cover perhaps the most powerful machine learning algorithm today: XGBoost (eXtreme Gradient Boosted trees). We'll talk about how they work, how to tune their parameters, and why they are so effective. Then, we'll apply it to the Iris data set to see it in action using a Jupyter notebook with Python.
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[@sb-jo2ch]

You have the _perfect_ voice and narration, something which I have missed in every other popular ML video lectures and courses.

[@daveherring1765]

the banyan tree is a perfect analogy. thanks! great video.

[@robloxgamercool]

I wish you were my teacher when i studied this in school..very clear and concise

[@srikarkodati961]

by far the best xgboost tutorial i have seen. Well done!

[@stevenlomon]

Sooooo smooth and to the point, wonderful video!!

[@ishana6083]

"it's so easy to use ,it's almost disturbing!", I literally LoL'ed at that! :D

[@shihamfarook7291]

Well explained, Thank you

[@valeriedrew6175]

Well explained. Very clear and easy to follow.

[@chalamrani1923]

Where do i get the course material to run the algorithm with sample data . I purchased the Udemy course as well but unable to find the Ml course folder.. Can you please guide to get that information

[@studyaccount9662]

Excellent demonstration, thanks!!!

[@SergioPolimante]

this video openned up a recursive dependence that took me to watch 213402 more videos. damn

[@almerchant7595]

could you kindly provide the link for the full playlist? thx

[@jisanhasnat6770]

Awesome explanation!

[@cutyoursoul4398]

Nice clear video, thanks :)

[@ekleanthony7997]

Really Nice video, great explanation. thanks!

[@taghredashry4633]

Very clear I love this tutorial!

[@ramnareshraghuwanshi516]

As your title says.. where did you explain "How it works"? you just explain pros and cons of model.

[@afreshpineapple]

In other words, its the wet dream of every Data Scientist?

[@davidd2702]

Thank you for your fabulous video! I enjoy it and understand well!
Could you tell me if the output from the xgb classifier gives 'confidence' in a specific output (allowing you to assign a class) ? or is this functionally equivalent to statistical probability of an event occuring?

[@gowiththeflo59]

The model outputs the predicted flower classes (which were converted from their names to index values 0, 1 and 2). He then uses accuracy_score to measure what % of predictions were correct and gets 1.0 ratio (100% correct predictions).
In this case XGBoost is being used as a classifier (which flower type 0, 1 or 2), not doing regression (predicting a probability or number like the predicted price of a House given some features about the house, for example)

[@aarondelarosa3146]

Why don't you try to forecast stock price using xgboost?

[@inv_master735]

Character In the video It's great, I like it a lot $$

[@dannyrodin1151]

Great video, but i'd wish you'd bothered to thoroughly explain the hyper parameters.

[@charlesglah5267]

very good.

[@rosieshi1354]

At least it is good introduction for beginners

[@shwetaredkar734]

If you could execute it using 10fold cv than it would be great. thanks.

[@DanKeeley]

not in a critical way, but is this a sales pitch for xgboost? :) hahhaha

[@alexanderkis9041]

so how it works?

[@beibit-ds]

This is more "How to use it" instead of "How it works"

[@usern8937]

Why don't you upload the notepad !

[@GeorgeWilliams-v9d]

Miller Ruth Williams Daniel Garcia Patricia

[@SaraSilva-zu7wn]

This is "How to use it", not "How it works".

[@vincentjean6756]

and that is perfectly fine!

[@soheiltehrani3792]

iris dataset?!!!
Really?!