Model evaluation

Regardless of method of creating regression model, linear:

val model = createLinearRegressionModel(scaledTrain)

or based on decision tree:

val model = createDecisionTreeRegressionModel(scaledTrain)

evaluation requires of using test data to calculate predictions and store them with actual prices:

val predictionsAndValues = scaledTest.map {
  point => (model.predict(point.features), point.label)
}

Mean house price:

scaledTest.map { x => x.label }.mean()

Max prediction error:

predictionsAndValues.map { case (p, v) => math.abs(v - p) }.max

Root mean squared error:

math.sqrt(predictionsAndValues.map { case (p, v) => math.pow((v - p), 2) }.mean())

Sample output can look like that:

Mean house price: 534198.5644402637
Max prediction error: 1710000.0
Root Mean Squared Error: 149589.65702292052