Very organized and clear with excellent examples that make it so easy to understand. Thank you!

I never comment on any KZfaq videos, but I am compelled to do here, because I learned most of my analyses for my dissertation following your tutorials. You're such a great tutor. Thank you so much.

Very well explained, sir. It was intuitive for beginners. The analogies are interesting as well.

Very organized and straight forward! Waiting other videos. Thank you for sharing this knowledge.

Very well explained, clear and concise. Thanks for taking your time

Perfect tutorial, I am using XGBoost and random forest to analyze some work. Perfect tutorials for me. Always appreciate your continuous efforts to share your knowledge through youtube.

Wow your explanotion is awesome!!! Dont stop plz

I really appreciate this explanation! Thank you!

Sreeni said we need to normalize, but I always thought we didn't need to do that with trees... Am I confused on something? Thanks for the video!!

Thanks, Sreeni sir for your valuable and knowledgeable content. Also, waiting for the next semantic segmentation series and also discusses the hyperparameters and their tuning, Time series analysis that will be highly helpful.

XGBoost with Regularized Rotations and Synthetic Feature Construction can approximate Deep NN deepness

This was very clear and useful! Do u have any link to your code? Also, could xgboost be used for linear regression aswell?

Thanks for the video and effort. Can you make a time series video using xgboost, or something with multiple features(lags, rolling mean, etc..)

It's not clear where did you get a dataset in a CSV format - the .zip archive from provided link includes only `wdbc.data` and `wdbc.names` files

The example of bagging is so funny and I can fully relate

Awesome Tutorial, Glad I got a great teacher..Thank you...

can you make a quick video on normalization and standardizetion for a image dataset

Thanks for the video. Something I noticed in the figure above that you might have missed is that in the figure you show the most evolved species has lighter hair than less evolved which could interpret a false impression that species with lighter hair are more evolved. It would be great if you could adjust the figure.

thanks for the video what we have to change if we want to use XGBRegressor() insted if classifier ? xgboost documantation is so confusing !

your explanation is so interesting haha, it helps me a lot understand the material

Magnificant!!! Thanks for clear explanation Sir.

Awesome video, thank you ! Greetings from Brazil!

Awesome comparison. Super thanks

Thank you so much for your explanation sir

Another case of data leakage. You can't scale X and then split it into test and train. The scaling needs to happen after the split.

if your explanations were a kaggle competition it would be top 1%

very useful video ! Thank you!

Nice T-Shirt 😃

Very helpful thank you!

A very useful episode, thanks sir

loved the arrange marriage example! Made it very intuitive and easy to understand. Thank you!

Wow that analogy! 😂 Amazingly apt lol!

Well explained. Thank you so much for the video.

Hi. I have a 1500x11 dataset and I am trying to see which out of cognitive ability, non-cognitive ability, family structure, parental involvement, and school characteristics predict academic performance (measured in terms of grades ranging from 1-5). Should I be using XG Boost for this problem or random forest? Thanks!

I have a question about the Xgboost algorithm. The question is how parallelization works in the Xgboost algorithm and explain me with an example.

What’s the method name? When are you presenting at NeurIPS? (I’ll be attending it :)

very clear thanks :)

dataset in the UCI link not avaiable now. could any one can provide update link?

The dataset has been removed from the website. is it possible to upload it?

thank you very much

Great!

Where can I get the exact .csv file?

Perfect

bro trying to share about life n forgot wts hes teaching 🤣🤣🤣🤣 only were i gt complete idea about xgboost tq

Looks like when you demo-ed random forest, you didn't comment out the xgb line, so you actually showed the fitting for xgb twice with the same results.

You're worth more money

Good one

can you talk about transformers please?

Merci !