Stanford classifier cross validation averaged or aggregate metrics

Question

With Stanford Classifier it is possible to use cross validation by setting the options in the properties file, such as this for 10-fold cross validation:

crossValidationFolds=10
printCrossValidationDecisions=true
shuffleTrainingData=true
shuffleSeed=1

Running this will output, per fold, the various metrics, such as precision, recall, Accuracy/micro-averaged F1 and Macro-averaged F1.

Is there an option to get an averaged or otherwise aggregated score of all 10 Accuracy/micro-averaged F1 or all 10 Macro-averaged F1 as part of the output?

In Weka, by default the output after 10-fold cross validation includes averaged metrics over all folds. Is such an option also available in Stanford Classifier? Having a final precision, recall or F1 score available and optimizing the parameters against it like in Weka is very useful, and I would like to do this with Stanford Classifier. How?

Answer 1

When I run with 10 folds, I am seeing that output. When I run this command:

java -cp "*" edu.stanford.nlp.classify.ColumnDataClassifier -prop examples/cheese2007.prop -crossValidationFolds 10

I see this in the output (after ### Fold 9)

[main] INFO edu.stanford.nlp.classify.ColumnDataClassifier - 181 examples in test set
[main] INFO edu.stanford.nlp.classify.ColumnDataClassifier - Cls 2: TP=109 FN=6 FP=7 TN=59; Acc 0.928 P 0.940 R 0.948 F1 0.944
[main] INFO edu.stanford.nlp.classify.ColumnDataClassifier - Cls 1: TP=59 FN=7 FP=6 TN=109; Acc 0.928 P 0.908 R 0.894 F1 0.901
[main] INFO edu.stanford.nlp.classify.ColumnDataClassifier - Accuracy/micro-averaged F1: 0.92818
[main] INFO edu.stanford.nlp.classify.ColumnDataClassifier - Macro-averaged F1: 0.92224 
[main] INFO edu.stanford.nlp.classify.ColumnDataClassifier - Average accuracy/micro-averaged F1: 0.93429
[main] INFO edu.stanford.nlp.classify.ColumnDataClassifier - Average macro-averaged F1: 0.92247