At 03:42 I was wondering if you were actually applying a `RandomForestRegressor` to a classification problem, but you've obviously corrected that in the Colab notebook linked in the description, and applied a `RandomForestClassifier`. But now I wonder: if `RandomForestClassifier` reaches a score of 0.9446875, and `XGBClassifier` reaches a score of `0.94325` - why would I choose Gradient Boosted Trees?
When you say the second tree is the negative gradient of the loss function w.r.t the previous output, do you mean that the second tree uses the negative gradient in place of the original dataset's response values?
Great video! I have a question, though. How do you add the gradient of the loss to a tree? What does that even mean? [EDIT] Also, what happens at test time? Is only the last learner used i.e. F(n)?
@@rafsunahmad4855 it depends on the company but usually junior data scientists are not expected to know more than linear regression and SQL. It's always good to know the math if you want to do a good job.
The explanation is good, but the background music is terrible. Not only distracting, but completely out of place. Not sure why they thought it was a good idea. Make videos focusing on content, and remove any background music.
Great explanation! But honetsly, this music make sit soo much harder to concentrate !! :S
Noted! Thanks a lot!
@@Econoscent could you re-upload without music please?
This is how clean teaching is done. Simple just the model's working in easy terms. Thanks very much!!
Wow, I can't believe they included all the important information in just 4 minutes with visuals and animations! Really good work.
This is awesome! Visuals are spot on! It's crazy how much quality information can be fit into 4 minutes
Thank you so much for the appreciation!
Background music is too loud.
Noted. Thanks!
After I saw video preview picture I said "AAAAAA It is so simple" grate explanation , thank you!
Great refreshers for me. I hope more people see this
Glad it helped!
Hi ! In the first step ( 1:37 ) , a single decision tree is fit. How do you do that? Using entropy method in datasets or Gini Impurity method?
You can use either!
At 03:42 I was wondering if you were actually applying a `RandomForestRegressor` to a classification problem, but you've obviously corrected that in the Colab notebook linked in the description, and applied a `RandomForestClassifier`. But now I wonder: if `RandomForestClassifier` reaches a score of 0.9446875, and `XGBClassifier` reaches a score of `0.94325` - why would I choose Gradient Boosted Trees?
When you say the second tree is the negative gradient of the loss function w.r.t the previous output, do you mean that the second tree uses the negative gradient in place of the original dataset's response values?
but in the google colab document the two models accuracy and the randomforest had a better one ?
Great video! I have a question, though. How do you add the gradient of the loss to a tree? What does that even mean?
[EDIT] Also, what happens at test time? Is only the last learner used i.e. F(n)?
Great video, keep em coming! I think you can slowly start making more in-depth videos too
Great video, so glad I found your channel. Thank you for putting in the effort to provide this content 👍
how do you compute the derivative? This is Frechet derivative here...
great work! such an underrated video (in terms of views)
Much appreciated!
Very clear explanation thank you so much!
Easy explanantions and great illustrations.Loved it
Thank you so much 😀
Wow! Thanku so much Mam . Really best visualization . I saw first time & liked videos so much.🥰😘
Most welcome 😊
Thank you very much along with provided actual code
Glad it helped
I subscribed! Great videos! Just wondering why there are still so many dislikes?
Amazing, just what i wanted, however the music is a bit distracting, at least for me.
Wonderful exposition!
Thank you! Great work on your channel as well!
loved the 'close enough' rage face.
This guy gets it!
Greate video!
Please lower the music volume next time ^_^
Thanks, will do!
is knowing the math behind algorithm must or just knowing that how algorithms works is enough? please please please give a reply.
It's not important to understand the math but it's important to understand the algorithm's limitations so you know when to apply it
In data science interview do the interviewer ask math behind algorithm?
@@rafsunahmad4855 it depends on the company but usually junior data scientists are not expected to know more than linear regression and SQL. It's always good to know the math if you want to do a good job.
Thank you very much❤️
loved the explanation!
Thank you! Do subscribe so you know when we put out new videos!
Great Explaination ! Thanks so much...
The music is very distracting
Thanks for the Great Content
Superb video! I would just tone down the music and choose a less upbeat melody (if any at all), as it's too much on par with voice imo
You just got a new subscriber!
Ditto!
Thanks a lot!
Thank you! That helped me a lot.
Glad it helped!
super well explained! thanks
You're welcome!
I love the music! What is it?
(loved the video too :) )
Hey man. Can't remember the track which I should be crediting here. Will do that in the future. It's from the TH-cam Music Library
Awesome illustrations and subscribed!! How do you make such animations?
Thanks a lot! We use a tool from Adobe called After Effects
way clearer than statquest
Great explanation!
Thank you!
this was great!
Thanks!
THANK YOU SO MUCH 🤍🤍🤍🤍🤍
You're welcome!
The music has a backgroung scraping sound that is a bit annoying.
Great video !!! Very clear and accurate explanation!!! Great selection of background music !!!
Thanks a lot for watching! Smash the like and subscribe button!
Not visual, but mathematic notational. Fail to show visually the trade-off in complexity, decision tree depth, and overfitting.
Gr8 Elaborate explanaton.
Thank you so much! Do subscribe for more videos
the music is silly and causes loss of focus
dios mío, qué buen video :'d
005 Jamie Forks
The explanation is good, but the background music is terrible. Not only distracting, but completely out of place. Not sure why they thought it was a good idea. Make videos focusing on content, and remove any background music.
Cummings Groves
Sophie Ridges
please remove the background music. One guy's favorite music is a poison for another guy!
Thomas Deborah Walker Nancy Hernandez Jose
Your derivative explanation was so poor that it simply confused
? This is exactly how derivatives work my bro
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Why the hell this irritating background music was needed?
a perfect video was ruined.
This was too much for me
couldn't watch the video just bc of the annoying music
Music is distracting and annoying!