XGBoost Made Easy | Extreme Gradient Boosting | AWS SageMaker

[@mohamedsaber9634]

One of the best contents on the XGBoot subject. SIMPLE yet DEEP into details.

[@behradbinaei7428]

After searching 2 days , Finally I learned GB algorithms. Thank you so much

[@ahmadnurokhim4168]

This is exactly what I need, I see the other videos didn't cover the general concept like this

[@robindong3802]

Thanks to Stemplicity, you make this profound algorithm easy to understand.

[@carsten7551]

I really enjoyed your video on XGBoost, Professor Ryan! This video made me feel much more comfortable with the model conceptually.

[@mathsalmath]

Thank you Prof. Ahmed for a visual explanation. Great video.

[@WilsonJoey]

Great explanation of xgboost regression. Nice job professor.

[@sirginirgin4808]

Excellent Explanation and to the point. Kindly keep up the good work Ryan.

[@johnpark7662]

Agreed, excellent presentation!

[@professor-ryanahmed]

Glad you liked it!

[@JIAmitdemwesen]

Very nice. I was quite confused in the beginning but the practical example help a lot to understand what is happening in this method.

[@scottlapierre1773]

One of the best, for sure! Thank you.

[@maheshmichael6955]

Beautifully Explained :)

[@user-wr4yl7tx3w]

Great presentation. Clear and well explained.

[@SimbarasheWilliamMutyambizi]

Wonderful explanation

[@ACTION206]

Very nice explanation

[@Ram-oj4gn]

wow great explanation..

[@mdgazuruddin214]

I think it's a tutorial on Gradient Boosting, Please make sure, and will be happy if you prove me wrong.

[@sudippandit1]

Your effort is great I really appreciate your efforts to make the things easy at a root level in this video. I would like to request to prepare one video like the same root level to make the idea of XGboost as easy as possible. How the Dmatrix, gamma and lambda parameters works to achieve the best model performance?

[@khawarshehzad487]

Excellent video! loved the explanation

[@sarolovito2838]

Really excellent explanation!

[@NadavBenedek]

The title says 'Gradient' but inside the video, where is the gradient mentioned?

[@marcoaerlic2576]

Thanks for the great content, very well explained.

[@aiinabox1260]

What youre saying is appllcable to Gradient boosting this is not xgboost .... You need to change the title as Gradient boosting .. xgboost u need to compute similarity score , gain & so on.

[@MsDarkzar]

good explanation! thank you very much!.

[@professor-ryanahmed]

Glad it was helpful!

[@ziadadel2003]

one of the best

[@davidzhang4825]

Great video! Curios to know the difference between XGboost and Light GBM

[@shrutichaubey2434]

great content

[@renee1187]

you just tell about gradient boosting what about extreme gradient boosting ? tittle is incorrect ....

[@aiinabox1260]

thanx for the fantastic explanation.... pl correct me if am wrong. my understanding is INITIAL model (average ) (A) -> residual -> Build an additional Tree to predict errors (B) -> with the combination of (A) & (B) it produces the target predicted value (P1); iteration 2 , this P1 (C) residuals -> predict errors (D) -> combination of C + D we get new predicted values...... Here the Tree B is called as weak learners and also called as Weak Learner. Am I correct ?

[@jkho2085]

Hi, it is a wonderful contents on XGboost. I am a final year student and i wish to write it inside the report. However, it is hard to find the paper to support it.... Any suggestion?

[@theforrester2780]

Thank you, I needed this

[@elchino356]

Great video!

[@professor-ryanahmed]

Glad you enjoyed it

[@HemanthGanesh]

Thanks much!!! Excellent explanation

[@fahadxraja2483]

sdf

[@firstkaransingh]

Link to xgboost video ?

[@thallamsairamya6843]

A novel xg boost tuned machine learning model for software bug prediction We need a video regarding this exactly what I request Plz make a video like that asap

[@gauravmalik3911]

Best explanation, btw how do we choose learning rate

[@carsten7551]

You can tinker around with the learning rate yourself to see how the model's accuracy improves depending on a larger or smaller learning rate. But keep in mind that very large or small learning rates may not be ideal.

[@NghiaDuongTrung-k7l]

How about another tree architecture when the root is from another feature? Let's say we start at the root of "is not Blue?"

[@KalyanAngara]

Dr. Ryan. How can I cite you? I am writing a report and would like to cite your teachings.

[@moleculardescriptor]

Something is not right in this lecture. If each subsequent tree is _the_same_, as shown here, then after 10 steps the 0.1 learning rate will be nullified, e.g. equivalent to the scaling = 1.0! In other words, no regularization. Hence, trees must be different, right?

[@charlesmonier7143]

this is not XGBoost. wrong title

[@GeorgeWilliams-v9d]

Harris Carol Harris Edward Jackson Jason

[@davidnassau23]

Please get a better microphone.