Random Forest Hyperparameter Tuning using RandomisedSearchCv | Machine Learning Tutorial

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Getting 100% Train Accuracy when using sklearn Randon Forest model? We will be using RandomisedSearchCv for tuning the parameters as it performs better. You will learn how to use Random Forest by optimising the hyperparameter or parameters.
By tuning you are most likely to avoid overfitting using Randon Forest.

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KunaalNaik

I think it is useless to run a RandomSearchCV after you've run a GridSearchCV. The Grid Search already contains all 48 combinations of parameters. It will tell you what that best combination is on the TRAIN set data after cross validation.

You run the RandomSearchCV first, so that it points you to a space where you should comb and look through a little more. Then you can use GridSearchCV to comb that through space or those parameters a little bit more specifically and more slowly...

nerassurdo

Sir, Thank you for sharing this valuable information. It helped me a lot.

ushajoy

Hi thanks for the video. what is a large dataset? in size?

Deivid

Brother can you make a video on how to replace vlookup in excel when there are multiple columns. like for example taking daily Price change and Volume change of stocks and seeing the difference between them as a OUTPUT.

yashmishra

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