Hello,
Gridsearch with CV is something like this at a high level:
for every combination of parameters:
for every partition of training data
split training into train_cv and test_cv
train_classifier(train_cv).predict(test_cv)
compute score
average score
if max so far, then update best params
I woud like to do something like this:
for every combination of parameters:
for every partition of training data
split training into train_cv and test_cv
scaler = StandardScaler()
scaler.fit(train_cv)
train_cv = scaler.transform(train_cv)
test_cv = scaler.transform(test_cv)
train_classifier(train_cv).predict(test_cv)
compute score
average score
if max so far, then update best params
basically, I would like to scale training data and test data (using training
data params) every time a CV train/test is generated.
Can someone suggest the best way to modify grid_search.py to do this?
Thank you,
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