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

Question

There is absolutely helpful class GridSearchCV in scikit-learn to do grid search and cross validation, but I don\'t want to do cross validataion. I want to do grid search wi

Answer 1

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 = g

print "OOB: %0.5f" % best_score 
print "Grid:", best_grid