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.
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?
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
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!
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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.
Why didn't I find this playlist b4 !! Great content. !
Amazing video, thank you for this!
My pleasure!
You are saving my life rn 🙏
You are amazing! Thank you!
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 ❤
permission to learn, miss. thank you
Can we use the same code for a regression task?
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?
Great. Please, add the previous video to the playlist.
Thank you for the heads up!
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
That is true👍
Excellent video! Could you add code for getting the out-of-bag accuracy metric from the random forest? Thank you!
thank you for the informative video 👍
You're very welcome Aiman!
How do we print the predictions so we can see what it looks like? Just "print(predictions)?"
That should work!
Please share the code
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!
why does mine say error index has 0?