Modern Scala Projects

Step 10 – evaluate Random Forest classifier 

In this section, we will test the accuracy of the model. We want to know how well our model performed. Any ML process is incomplete without an evaluation of the classifier.

That said, we perform an evaluation as a two-step process:

1. Evaluate the model output
2. Pass in three hyperparameters:

val modelOutputAccuracy: Double = new MulticlassClassificationEvaluator()

Set the label column, a metric name, the prediction column label, and invoke evaluation with the validatedTestResults dataset. 

Note the accuracy of the model output results on the testing dataset from the modelOutputAccuracy variable.

The other metrics to evaluate are how close the predicted label value in the 'predicted' column is to ...