When Should You Use Random Forests?

[@RichardOnData]

What's a time you used random forests? How well did they do the job for you?

[@gopikrishnagavini6583]

99I

[@rhn122]

Awesome video! Like how you put the content of your video inside the description too, actually making me more eager to watch the whole video. Really a hidden gem of the Data Science KZbin channel!

[@RichardOnData]

Thank you, it's a growing work in progress! I'm glad you enjoy reading the description as I definitely try to put some effort into those.

[@paulcurry8383]

Using RF r/n for Snow Detection w/ satellite data. Thanks for the video! 😊

[@thats-no-moon]

You never blink. It’s amazing. (And great video, too.)

[@salmaasghar1674]

It was a really helpful video as I am working in RF and had no idea about its need and use. But clearly mentioned a few important things.

[@RichardOnData]

Glad it was helpful!

[@windkl]

Exceptional random forest description ! Thank you !!

[@wb7779]

That was a really good explanation. Short and powerful.

[@chillywings]

Hi Richard. No idea if you're still monitoring the comments, but I have a question. I have data set which contains categorical and continuous data, and many of the continuous features are expected to be highly correlated. This seems like the exact scenario you described where RFs are not ideal. Using an RF model has been proposed likely because of familiarity and the interpretability factor you mentioned. I will look into the conditional inference trees. However, I wonder if perhaps a different ML model would be better suited for this type of data?

[@teatea5528]

This guy looks like a real nerd but is interesting to watch simultaneously.

[@omarlghali8908]

Hi I have a question, which one could perform better for regression random forest or neural network ?

[@RichardOnData]

It depends a great deal on the problem and data in question. If your goal is inference, you can get feature importance ranking using random forest; generally neural networks are quite renowned however for strong predictive capability.

[@americo_alves]

F*k! You're good. I saw the video on 1.25 speed and this is like wonderful on how easily one can grasp it from end-to-end! Kudos!

[@bijoydey479]

Sir, can use random forest when my dependant variable is bouned between 0 to 1. That is censored

[@jasonhamm7174]

Amazing video! Definitely going to check out more of your videos! Keep it up :)

[@RichardOnData]

Awesome, thank you!

[@fernandojackson7207]

Richard, great video. I had a question: linearity of a model, as I understand it is defined by linearity on the parameters/coefficients and not the variable itself. Is this correct? If so, do you know of examples of data that cannot be modeled linearly in this sense?

[@nicolasgarcia123]

Thank you for the video!

[@RichardOnData]

My pleasure!

[@mandhavenkatreddy]

Awesome explanation Thanks a lot 😀

[@RichardOnData]

My pleasure, thanks for watching!

[@manhalrahman5785]

Killer look man

[@RichardOnData]

Hah! Well thank you!

[@hannahw904]

really good video, thank you

[@RichardOnData]

My pleasure! I don't have ton of videos like this particular one, so if you want to see similar ones let me know and I'll make it happen!

[@75hilmar]

Intense. Like.

[@Vicdeba]

Amazing!

[@RichardOnData]

Thank you! Cheers!

[@alberthiggins383]

hi richard

[@Chuukwudi]

Clown. Hahahahahaha First time coming to your channel and you are telling me about amazon forests. lol. I was like who is this guy?

[@RichardOnData]

Hah! I like to incorporate some humor, however ridiculous, into the channel just to spice things up and keep you guessing.

[@marinebiogirl]

@@RichardOnData That's why I subscribed! This was in my required viewing for homework in a course on Data Science, btw!

[@RTX_valorant]

😑😑😑😑😑😑😑😑😑😑😑😑😑😑😑😑😑😑

[@Ray-lh3oc]

s

[@muhammednishadkandathsaith9203]

??? 😂😂😂😂😂

[@officialegemen]

omg get to the point already man