Hi,
I have a limited dataset and hence want to learn the parameters and also
evaluate the final model.
>From the documents it looks that nested cross validation is the way to do
it. I have the code but still I want to be sure that I am not overfitting
any way.
pipeline=Pipeline([('scale', preprocessing.StandardScaler()),('filter',
SelectKBest(f_regression)),('svr', svm.SVR())]
C_range = [0.1, 1, 10, 100]
gamma_range=numpy.logspace(-2, 2, 5)
param_grid=[{'svr__kernel': ['rbf'], 'svr__gamma': gamma_range,'svr__C':
C_range}]
grid_search = GridSearchCV(pipeline, param_grid=param_grid,cv=5)
Y_pred=cross_validation.cross_val_predict(grid_search, X_train,
Y_train,cv=10)
correlation= numpy.ma.corrcoef(Y_train,Y_pred)[0, 1]
please let me know if my understanding is correct.
This is 10*5 nested cross validation. Inner folds CV over training data
involves a grid search over hyperparameters and outer folds evaluate the
performance.
Thanks,
Amita--
Amita Misra
Graduate Student Researcher
Natural Language and Dialogue Systems Lab
Baskin School of Engineering
University of California Santa Cruz
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