This would be much clearer if you provided some code, but I think I get
what you're saying.
The final GridSearchCV model is trained on the full training set, so the
fact that it perfectly fits that data with random forests is not altogether
surprising. What you can say about the parameters is that they are also the
best parameters (among those searched) for the RF classifier to predict the
held-out samples under cross-validation.
On 12 May 2016 at 19:53, A neuman <themagenta...@gmail.com> wrote:
> Hello everyone,
>
> I'm having a bit trouble with the parameters that I've got from
> gridsearchCV.
>
>
> For example:
>
> If i'm using the parameter what i've got from grid seardh CV for example
> on RF oder k-nn and i test the model on the train set, i get everytime an
> AUC value about 1.00 or 0.99.
> The dataset have 1200 samples.
>
> Does that mean that i can't use the Parameters that i've got from the
> gridsearchCV? Cause it was in actually every case. I already tried the
> nested-CV to compare the algorithms.
>
>
> example for RF with the values i have got from gridsearchCV (10-fold):
>
> RandomForestClassifier(n_estimators=200,oob_score=True,max_features=None,random_state=1,min_samples_leaf=
> 2,class_weight='balanced_subsample')
>
>
> and then i'm just using *clf.predict(X_train) *and test it on the*
> y_train set. *
>
> the AUC-value from the clf.predict(X_test) are about 0.73, so there is a
> big difference from the train and test dataset.
>
> best,
>
>
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