Hello All,
I am new to scikitlearn and ML, and trying to train my model using random
forest and gradient boosting trees regressors. I was wondering what is the
best way to do hyperparameter tuning, shall I use GridSearchCV or
RandomisedSearchCV? I have read that the performance of RandomiseSeacrhCV
is almost same as GridSearchCV (most of the times). If I go with
RandomisedSearchCV then what should be the range of values for different
parameters? How will I know that the range I am selecting is the correct
one?
Also, what about the number of estimators? In the GridSearchCV or
RandomisedSearchCV, shall I start with a low value and then after selecting
other parameters, I will choose a large number of estimators for fitting
purposes. Am I right?
Shall I always use early stopping, no matter if I use Grid search or
Randomised Search?
P.S: Training data: Number of Inputs = 6
Number fo Outputs = 1
Number of samples (rows) = 8526
testing data: Number of samples (rows) = 1416
Thanks
Kindest Regards
Waseem
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