Introduction to Random Forest | Intuition behind the Algorithm 

you are great teacher....great data scientist....hats off to you....because of you all the basic , small n small concepts getting cleared...you are faaabbb

I m a great fan of u & ur channel. Thanku so much for working hard. Itna mast Random forest shayad hi kisine explain kiya hoga youtube par. Not only Random Forest actually all other algorithms or different concepts in Data science , all videos of urs r the best of the best.

Best ever channel & best ever teacher for data science . Thanku very very much.

Hi, thanks for the great explanation but just a silly mistake I think after we did column sampling, we will need to give data point for prediction in same order as the features selected at 30:00

It is best Channal for data Science It will grow 1M in 2023 i swear

the way you teach is absolutely amazing , keep up the good work ,thankyou

Your explanation on each small point is next level! Great job sir.

Great content totally diffrent way of teaching!! Mark my word nobody spent so much time on a Single project but here the story is diffrent!!

Your videos on ML are amazing. Following this playlist!!

Bahut badiya sir. God bless you 🙏🙏

massive respect ❤, you are a great teacher

I think there is some mistake while using column sampling, in a prediction part you are passing the same input array to all the decision trees while it should be different according to the sampled dataframe.

Very Well Explained Thanks Sir

there is a small mistake in the column_row sampling(last one). df1,2,3 have different features which are trained on different models and while performing the prediction, you passed he same features for all three models and took majority(aggregation). apart from this rest is awesome. I have a question. lets say I run the random forest by giving my dataset with columns from F1 to F10 with target variable Y. I ran some sklearn model or something. i did train test split and i did fit and now, I want to perform prediction. since, each DT model in RF has different and only few cols(let's say 50%) and my test data will have all the cols. Are the test data features gonna be passed to respective DT model with respective features that the model was trained on? or is there some other mechanism? please explain.

best of the best!

I m a great fan of u & ur channel

sir hum random features selection karenge aur us par value prediction bhi karenge dt se lekin agar hume testing / input data whole features ka mila toh prediction kaise hoga ? i think row sampling is fine but features sampling not understood .

THANK YOU 3000

I am not able to find the link for the visualisation tool

Hi, i have a small question about decision trees. Is it ok to have a decision tree with a max depth of 7, bcs i noticed how it produced a big tree but also had the better accuracy training and test score than if I had reduced the max depth.

While teaching preprocessing you mentioned that we check for duplicate data (rows) and drop it as it can create problem while building model then how random forest handles the duplicate data which comes from sampling with replacement techniques.

Thank u sir

You are great

I am still confused on sampling without replacement, if we have 10 features and we selecting let say 5 features for each DT, then my 10 features will be exhausted in 2 DTs only. Then how come it's distributing in i.e. 100 DTs? Same with row sampling we are giving 25% of rows to each DTs then it should be exhausted after 4 DTs, then how come we are training the other DTs. Please help me with this, I am totally confused with 'without replacement' option.

Wow

finished watching

column sampling function not working . can anybody resolve it ?

Best one

ca we take two output in RF

I think the explanation of bootstrapping is wrong in the video. As per Wikipedia (en.wikipedia.org/wiki/Bootstrap_aggregating) , in bootstrapping process the size of the sample dataset has to be same as the original dataset and it is not a smaller subset of the original dataset as explained in the video. I think what you have explained (creating smaller samples from the original dataset) is concept of bootstrapping but applicable from statistics standpoint and not in context of Random Forests. Still I will be grateful if you can kindly explain your reasoning with some related articles.

sir please provide notes for this ml playlist

How come 65th video after 62nd(ensemble technique) ??

This video was a bit confusing ngl

Since you're speaking Indian, at least write the title in Indian!!! So we avoid wasting our time!