Is there easy way to grid search without cross validation in python?

I would really advise against using OOB to evaluate a model, but it is useful to know how to run a grid search outside of GridSearchCV() (I frequently do this so I can save the CV predictions from the best grid for easy model stacking). I think the easiest way is to create your grid of parameters via ParameterGrid() and then just loop through every set of params. For example assuming you have a grid dict, named "grid", and RF model object, named "rf", then you can do something like this:

for g in ParameterGrid(grid):    rf.set_params(**g)    rf.fit(X,y)    # save if best    if rf.oob_score_ > best_score:        best_score = rf.oob_score_        best_grid = gprint "OOB: %0.5f" % best_score print "Grid:", best_grid

One method is to use ParameterGrid to make a iterator of the parameters you want and loop over it.

Another thing you could do is actually configure the GridSearchCV to do what you want. I wouldn't recommend this much because it's unnecessarily complicated.
What you would need to do is:

• Use the arg cv from the docs and give it a generator which yields a tuple with all indices (so that train and test are same)
• Change the scoring arg to use the oob given out from the Random forest.

See this link:https://stackoverflow.com/a/44682305/2202107

He used cv=[(slice(None), slice(None))] which is NOT recommended by sklearn's authors.