How to implement Random Forest from scratch with Python

[@annawilson3824]

Great! To make it completely awesome, I guess n_features should be random as well, because in RF, the "random" aspect comes from two main sources: -Each tree is built from a random subset of the data (known as bootstrap sampling). -At each split in the tree, a random subset of features is considered.

[@VritanshKamal]

Why didn't I find this playlist b4 !! Great content. !

[@Igormenphito]

Amazing video, thank you for this!

[@AssemblyAI]

My pleasure!

[@no4ura459]

I've looked to DT and RF videos and they are very cool !!! By the way will you guys plan to upload video on gradient boosting?? Pleaaaseee ❤

[@franckchen233]

You are saving my life rn 🙏

[@KitkatTrading2024]

You are amazing! Thank you!

[@AHMADKELIX]

permission to learn, miss. thank you

[@bzvn3200]

Hi, it's a good video, but I want to ask why you didn't implement the Random Subspace Method? Without it, it turns out that you have implemented bagging over trees. The Random Subspace Method is very important because it reduces error correlation between basic algorithms in random forest, which reduces variance of errors

[@MahmouudTolba]

That is true👍

[@zelcadiana1337]

Hi. I am using random forest regression models to predict the mortality rate. My features have different dimensions, like millions, percents, thousands, etc. Do I need to do a standardization on my data before starting to built the models? Or any other kind of data transformation?

[@mohamedhendy5556]

Can we use the same code for a regression task?

[@pawlyk]

Great. Please, add the previous video to the playlist.

[@AssemblyAI]

Thank you for the heads up!

[@MrBaik123]

thank you for the informative video 👍

[@AssemblyAI]

You're very welcome Aiman!

[@thomaswolff3261]

Excellent video! Could you add code for getting the out-of-bag accuracy metric from the random forest? Thank you!

[@exometria]

How do we print the predictions so we can see what it looks like? Just "print(predictions)?"

[@AssemblyAI]

That should work!

[@geethamegharaj5556]

Please share the code

[@sanpavlovich9726]

How about np.random.choice(n_samples, n_samples // 3)? It will correspond to random subsamples method and help to decrease correlation between trees, so it should improve accuracy. And thank you for video!

[@spitfirelast8761]

why does mine say error index has 0?