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From Nirav Patel <npa...@xactlycorp.com>
Subject Spark ML - CrossValidation - How to get Evaluation metrics of best model
Date Tue, 01 Nov 2016 12:10:15 GMT
I am running classification model. with normal training-test split I can
check model accuracy and F1 score using MulticlassClassificationEvaluator.
How can I do this with CrossValidation approach?
Afaik, you Fit entire sample data in CrossValidator as you don't want to
leave out any observation from either testing or training. But by doing so
I don't have anymore unseen data on which I can run finalized model on. So
is there a way I can get Accuracy and F1 score of a best model resulted
from cross validation?
Or should I still split sample data in to training and test before running
cross validation against only training data? so later I can test it against
test data.

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