Machine Learning with Scala Quick Start Guide by Md. Rezaul Karim

Author:Md. Rezaul Karim [Md. Rezaul Karim]
Language: eng
Format: epub
Tags: COM037000 - COMPUTERS / Machine Theory, COM018000 - COMPUTERS / Data Processing, COM062000 - COMPUTERS / Data Modeling and Design
Publisher: Packt
Published: 2019-04-30T21:09:52+00:00

However, based on the preceding prediction DataFrame, it is really difficult to guess the classification's accuracy. But in the second step, the evaluation is done using BinaryClassificationEvaluator as follows:

val accuracy = evaluator.evaluate(predictions)

println("Classification accuracy: " + accuracy)

This will provide an output with an accuracy value:

Accuracy: 0.8441663599558337

So, we get about 84% classification accuracy from our binary classification model. Just like with SVM and LR, we will observe the area under the precision-recall curve and the area under the receiver operating characteristic (ROC) curve based on the following RDD, which contains the raw scores on the test set:

val predictionAndLabels = predictions

.select("prediction", "label")

.rdd.map(x => (x(0).asInstanceOf[Double], x(1)

.asInstanceOf[Double]))

The preceding RDD can be used for computing the previously mentioned two performance metrics:

val metrics = new BinaryClassificationMetrics(predictionAndLabels)

println("Area under the precision-recall curve: " + metrics.areaUnderPR)

println("Area under the receiver operating characteristic (ROC) curve: " + metrics.areaUnderROC)

In this case, the evaluation returns 84% accuracy but only 67% precision, which is much better than that of SVM and LR:

Area under the precision-recall curve: 0.6665988000794282

Area under the receiver operating characteristic (ROC) curve: 0.8441663599558337

Then, we calculate some more metrics, for example, false and true positive, and false and true negative, as these predictions are also useful to evaluate the model's performance:

val TC = predDF.count() //Total count

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