public static double MeanAbsoluteError(double[][] expected, double[][] predicted)
{
var loss = new AbsoluteLoss(expected);
loss.Mean = true;
return(loss.Loss(predicted));
}
public static double MeanAbsoluteError(double[] expected, double[] predicted)
{
var e = new double[1][] { expected };
var p = new double[1][] { predicted };
var loss = new AbsoluteLoss(e);
loss.Mean = true;
return(loss.Loss(p));
}
private static void DisplayTrainingResults(string name, LearnerMatrix learnerMatrix,
Frame <int, string> testData)
{
Console.WriteLine($"{name}:");
var labels = testData["median_house_value"].Values.ToArray();
var predictions = learnerMatrix.Transform(testData);
var rmse = Math.Sqrt(new SquareLoss(labels).Loss(predictions));
var mae = new AbsoluteLoss(labels.ToJagged()).Loss(predictions.ToJagged()) / labels.Length;
var range = Math.Abs(labels.Max() - labels.Min());
Console.WriteLine($"Label range: {range}");
Console.WriteLine($"RMSE: {rmse} {rmse / range * 100:0.00}%");
Console.WriteLine($"MAE: {mae} {mae / range * 100:0.00}%");
Console.WriteLine();
}