Supossed I have a Pipeline like this:
val tokenizer = new Tokenizer().setInputCol("tweet").setOutputCol("words")
val hashingTF = new HashingTF().setNumFeatures(1000).setInputCol("words").setOutputCol("features")
val idf = new IDF().setInputCol("features").setOutputCol("idffeatures")
val nb = new org.apache.spark.ml.classification.NaiveBayes()
val pipeline = new Pipeline().setStages(Array(tokenizer, hashingTF, idf, nb))
val paramGrid = new ParamGridBuilder().addGrid(hashingTF.numFeatures, Array(10, 100, 1000)).addGrid(nb.smoothing, Array(0.01, 0.1, 1)).build()
val cv = new CrossValidator().setEstimator(pipeline).setEvaluator(new BinaryClassificationEvaluator()).setEstimatorParamMaps(paramGrid).setNumFolds(10)
val cvModel = cv.fit(df)
As you can see I defined a CrossValidator using a MultiClassClassificationEvaluator. I have seen a lot of examples getting metrics like Precision/Recall during testing process but these metris are gotten when you use a different set of data for testing purposes (See for example this documentation).
From my understanding, CrossValidator is going to create folds and one fold will be use for testing purposes, then CrossValidator will choose the best model. My question is, is possible to get Precision/Recall metrics during training process?
Well, the only metric which is actually stored is the one you define when you create an instance of an Evaluator. For the BinaryClassificationEvaluator this can take one of the two values:
areaUnderROC areaUnderPR with the former one being default, and can be set using setMetricName method.
These values are collected during training process and can accessed using CrossValidatorModel.avgMetrics. Order of values corresponds to the order of EstimatorParamMaps (CrossValidatorModel.getEstimatorParamMaps).