Database Reference
In-Depth Information
nb.run(input)
}
val nbResults = Seq(0.001, 0.01, 0.1, 1.0, 10.0).map {
param =>
val model = trainNBWithParams(dataNB, param)
val scoreAndLabels = dataNB.map { point =>
(model.predict(point.features), point.label)
}
val metrics = new
BinaryClassificationMetrics(scoreAndLabels)
(s"$param lambda", metrics.areaUnderROC)
}
nbResults.foreach { case (param, auc) => println(f"$param,
AUC = ${auc * 100}%2.2f%%")
}
The results of the training are as follows:
0.001 lambda, AUC = 60.51%
0.01 lambda, AUC = 60.51%
0.1 lambda, AUC = 60.51%
1.0 lambda, AUC = 60.51%
10.0 lambda, AUC = 60.51%
We can see that
lambda
has no impact in this case, since it will not be a problem if the
combination of feature and class label not occurring together in the dataset.
