Hey I was just wondering how you would go about printing the prediction instead of printing the the accuracy of the prediction?
# evaluate model with standardized dataset
numpy.random.seed(seed)
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, epochs=50, batch_size=5, verbose=0)))
pipeline = Pipeline(estimators)
kfold = KFold(n_splits=10, random_state=seed)
results = cross_val_score(pipeline, X, Y, cv=kfold)
print("Standardized: %.2f (%.2f) MSE" % (results.mean(), results.std()))
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I'd need to see more of your code I think.
If you're using scikit-learn, then you do clf.predict([INPUTDATA]), if clf is the name of your classifier.
-Harrison 7 years ago
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