public void ProcessPredictionOutput(PredictionResult predictionResult)
{
int incorrectPredictions = 0;
int correctPredictions = 0;
for (int passengerIndex = 0; passengerIndex < 800; passengerIndex++)
{
var passenger = predictionResult.Passengers[passengerIndex];
var survived = passenger.PassengerSurvived;
//var passengerPredicted =
// predictionResult.PredictionModel.Model.Predict<Passenger>(passenger);
var passengerPredicted =
predictionResult.PredictionModel.Predict<Passenger>(passenger);
if (survived != passengerPredicted.PassengerSurvived)
incorrectPredictions++;
else
correctPredictions++;
//Console.WriteLine(
// "The actual = {0}, prediction = {1}",
// survived.ToString(), passengerPredicted.PassengerSurvived);
}
var percentageCorrect = (correctPredictions / 800.0) * 100;
var percentageIncorrect = (incorrectPredictions / 800.0) * 100;
Console.WriteLine("Out of 800 predictions, the engine predicted {0}% correctly and {1}% incorrectly", percentageCorrect, percentageIncorrect);
Console.ReadKey();
}
public PredictionResult GeneratePredictionModel(string dataPath)
{
if (string.IsNullOrEmpty(dataPath))
throw new ArgumentNullException("dataPath");
var generator = CreateDecisionTreeGenerator();
if (generator == null)
throw new NullReferenceException("The decision tree generator cannot be null.");
var data = Passenger.LoadData(dataPath, true);
var model = generator.Generate(data);
//var model = Learner.Learn(data, .90, 1000, generator);
var predictionResult = new PredictionResult {Passengers = data, PredictionModel = model};
return predictionResult;
}
