Setting Cross Validation parameter in Grid Search

Question

I am trying to set a RandomForestClassification inside a GridSearch

rfc_model = RandomForestClassifier(n_estimators = 5, max_depth = 3 )

gs = grid_search.GridSearchCV(estimator = rfc_model,
                             param_grid = {'n_estimators': [i for i in range(1,52,10)],
                                          "max_depth": [3, 5],
                                          "bootstrap": [True, False],
                                          "criterion": ["gini"]},
                             cv = cross_val_score(rfc_model,X, y, scoring='roc_auc'))

gs.fit(X, y)
gs.grid_scores_
print gs.best_estimator
print gs.best_score_

I get the error

TypeError: 'numpy.float64' object is not iterable

Obviously I am learning, so any comments are welcome.

stackoverflow.com/a/16865814/7714663 this might help you

this.girish
– this.girish

2017-05-08 23:31:14 +00:00
Commented May 8, 2017 at 23:31 — this.girish
– this.girish, Commented May 8, 2017 at 23:31

Horacio Nesman · Accepted Answer · 2017-05-09 00:47:35Z

1

Ok, I found the problem, I was using the wrong method(Is it alright to call it method?) for Cross Validation, below the solution:

gs = grid_search.GridSearchCV(estimator = model,
                             param_grid = {'n_estimators': [i for i in range(1,52,10)],
                                          "max_depth": [3, 5],
                                          "bootstrap": [True, False],
                                          "criterion": ["gini"]},
                             cv = cross_validation.KFold(n=len(X), n_folds=10), scoring='roc_auc')

answered May 9, 2017 at 0:47

Horacio Nesman

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