static void Main(string[] args)
{
var dataset = @"C:\Users\mert.ceken\source\repos\190610_salary_pred\190610_salary_pred\maas-veri.csv";
var testDataset = @"C:\Users\mert.ceken\source\repos\190610_salary_pred\190610_salary_pred\maas-veri-test.csv";
var pipeline = new LearningPipeline
{
new TextLoader(dataset).CreateFrom <SalaryData>(useHeader: true, separator: ','),
new ColumnConcatenator("Features", "YILLAR"),
new GeneralizedAdditiveModelRegressor()
};
Console.WriteLine("..........Eğitiliyor..........");
var model = pipeline.Train <SalaryData, SalaryPrediction>();
Console.WriteLine(Environment.NewLine);
Console.WriteLine("..........Değerlendiriliyor..........");
var testData = new TextLoader(testDataset).CreateFrom <SalaryData>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"Karekök Ortalama: {metrics.Rms}");
Console.WriteLine($"R^2: {metrics.RSquared}");
Console.WriteLine(Environment.NewLine);
Console.WriteLine("..........Tahmin..........");
var prediction = model.Predict(new SalaryData {
YILLAR = TAHMIN_EDILECEK_YIL
});
Console.WriteLine($"{TAHMIN_EDILECEK_YIL} yıl sonraki maaşınız {String.Format("{0:C}", prediction.PredictedSalary)}");
Console.ReadLine();
}
static void Main(string[] args)
{
var pipeline = new LearningPipeline
{
new TextLoader("SalaryData.csv").CreateFrom <SalaryData>(useHeader: true, separator: ','),
new ColumnConcatenator("Features", "YearsExperience"),
new GeneralizedAdditiveModelRegressor()
};
Console.WriteLine("--------------Training----------------");
var model = pipeline.Train <SalaryData, SalaryPrediction>();
// Evaluate
Console.WriteLine(Environment.NewLine);
Console.WriteLine("--------------Evaluating----------------");
var testData = new TextLoader("SalaryData-test.csv").CreateFrom <SalaryData>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"Root Mean Squared: {metrics.Rms}");
Console.WriteLine($"R^2: {metrics.RSquared}");
// Predict
Console.WriteLine(Environment.NewLine);
Console.WriteLine("--------------Predicting----------------");
var prediction = model.Predict(new SalaryData {
YearsExperience = PREDICTION_YEARS
});
Console.WriteLine($"After {PREDICTION_YEARS} years you would earn around {String.Format("{0:C}", prediction.PredictedSalary)}");
Console.ReadLine();
}
static void Main(string[] args)
{
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader("SalaryData.csv")
.CreateFrom <SalaryData>(useHeader: true, separator: ','));
pipeline.Add(new ColumnConcatenator("Features", "YearsExperience"));
pipeline.Add(new GeneralizedAdditiveModelRegressor());
var model = pipeline.Train <SalaryData, SalaryPrediction>();
var testData = new TextLoader("SalaryData-test.csv")
.CreateFrom <SalaryData>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"RMS - {metrics.Rms}");
Console.WriteLine($"R^2 - {metrics.RSquared}");
var prediction = model.Predict(new SalaryData {
YearsExperience = 7
});
Console.WriteLine($"Salary - {prediction.PredictedSalary}");
Console.ReadLine();
}
/// <summary>
/// Ussing passed testing data and model, it calculates model's accuracy.
/// </summary>
/// <returns>Accuracy of the model.</returns>
public RegressionMetrics Evaluate(PredictionModel <BikeSharingDemandSample, BikeSharingDemandPrediction> model, string testDataLocation)
{
var testData = new TextLoader(testDataLocation).CreateFrom <BikeSharingDemandSample>(useHeader: true, separator: ',');
var metrics = new RegressionEvaluator().Evaluate(model, testData);
return(metrics);
}
public static void Trainer()
{
var pipeline = new LearningPipeline
{
new TextLoader(trainingDataFilePath).CreateFrom <InsurerBillData>(useHeader: true, separator: ','),
//new ColumnCopier(("Score", "Label")),
new CategoricalOneHotVectorizer("ChannelName", "ProductId"),
new ColumnConcatenator("Features", "ChannelName", "ProductId"),
new FastTreeRegressor()
//new GeneralizedAdditiveModelRegressor(),
};
var model = pipeline.Train <InsurerBillData, PredictedInsurerBill>();
var testData = new TextLoader(testDataFilePath).CreateFrom <InsurerBillData>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"RMS - {metrics.Rms}");
Console.WriteLine($"R^2 - {metrics.RSquared}");
var test = new InsurerBillData {
ChannelName = ""
};
var prediction = model.Predict(test);
Console.WriteLine($"Predicted SPV - {prediction.SPV}");
Console.ReadLine();
}
static void Main(string[] args)
{
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader("SalaryData.csv").CreateFrom <SalaryData>(useHeader: true, separator: ','));
pipeline.Add(new ColumnConcatenator("Features", "YearsExperience"));
pipeline.Add(new GeneralizedAdditiveModelRegressor());
var model = pipeline.Train <SalaryData, SalaryPrediction>();
var testData = new TextLoader("SalaryData-test.csv").CreateFrom <SalaryData>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(model, testData);
Console.WriteLine("RMS - " + metrics.Rms);
Console.WriteLine("R^2 - " + metrics.RSquared);
float yearsExperience = 7;
var prediction = model.Predict(new SalaryData()
{
YearsExperience = yearsExperience
});
Console.WriteLine("Salary prediction for employee with " + yearsExperience + " years experience is " + prediction.PredictedSalary);
Console.ReadLine();
}
public static void GetMyPrediction()
{
Console.WriteLine("Begin ML.NET demo run");
Console.WriteLine("Income from age, sex, politics");
var pipeline = new LearningPipeline();
string dataPath = AppDomain.CurrentDomain.BaseDirectory + "/PeopleData.txt";
pipeline.Add(new TextLoader(dataPath).
CreateFrom <myLottery>(separator: ' '));
pipeline.Add(new ColumnCopier(("Income", "Label")));
//pipeline.Add(new CategoricalOneHotVectorizer("Politic"));
pipeline.Add(new ColumnConcatenator("Features", "pre10",
"pre9", "pre8", "pre7", "pre6", "pre5", "pre4", "pre3"
, "pre2", "pre1"));
var sdcar = new StochasticDualCoordinateAscentRegressor();
sdcar.MaxIterations = 1000;
sdcar.NormalizeFeatures = NormalizeOption.Auto;
pipeline.Add(sdcar);
// pipeline.N
Console.WriteLine("\nStarting training \n");
var model = pipeline.Train <myLottery, myPrediction>();
Console.WriteLine("\nTraining complete \n");
string modelPath = AppDomain.CurrentDomain.BaseDirectory + "/IncomeModel.zip";
Task.Run(async() =>
{
await model.WriteAsync(modelPath);
}).GetAwaiter().GetResult();
var testData = new TextLoader(dataPath).
CreateFrom <myLottery>(separator: ' ');
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(model, testData);
double rms = metrics.Rms;
Console.WriteLine("Root mean squared error = " +
rms.ToString("F4"));
Console.WriteLine("Income age 40 conservative male: ");
myLottery newPatient = new myLottery()
{
pre10 = 6824298f,
pre9 = 2589916f,
pre8 = 2602089f,
pre7 = 2915497f,
pre6 = 8507838f,
pre5 = 7679324f,
pre4 = 607461f,
pre3 = 5806877,
pre2 = 6776442f,
pre1 = 9975203
};
myPrediction prediction = model.Predict(newPatient);
float predIncome = prediction.Income;
Console.WriteLine("Predicted income = $" +
predIncome.ToString("F2"));
Console.WriteLine("\nEnd ML.NET demo");
Console.ReadLine();
}
internal override ReturnObj <RegressionMetrics> EvaluateModel <T, TK>(PredictionModel <T, TK> model, string testDataFilePath)
{
var evaluator = new RegressionEvaluator();
var testData = new TextLoader(testDataFilePath).CreateFrom <T>();
return(new ReturnObj <RegressionMetrics>(evaluator.Evaluate(model, testData)));
}
private static void Evaluate(PredictionModel <TaxiTrip, TaxiTripFaresPrediction> model)
{
var testData = new TextLoader(_testdatapath).CreateFrom <TaxiTrip>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"RSquared = {metrics.RSquared}");
}
public RegressionMetrics EvaluateModel(PredictionModel <Fatalities, FatalitiesPrediction> model)
{
var testData = new TextLoader(TestDataPath).CreateFrom <Fatalities>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
return(metrics);
}
public static double Evaluate(PredictionModel <WorldCupData, WorldCupPrediction> model)
{
var testData = new TextLoader(_testDataPath).CreateFrom <WorldCupData>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
return(metrics.Rms);
}
static void Main(string[] args)
{
//https://www.youtube.com/watch?v=8gVhJKszzzI
#region
//Setup the algorithm
//LearningPipeline from Microsoft.ML;
//TextLoader from Microsoft.ML.Data
//ColumnConcatenator from Microsoft.ML.Transforms;
//GeneralizedAdditiveModelRegressor from Microsoft.ML.Trainers;
//TextLoader connects to CSV file
//separator (delimiter) needs single quotes for some reason
//Create from is generic so can take any generic derrived type
//ColumnConCatenator concatates your input data into one column, calls it 'features', and pass in the column to this - we only have one - YearsExperience
//error I got - features needed to be capital first as per namign convention - threw exception when not
#endregion
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader("SalaryData.csv").CreateFrom <SalaryData>(useHeader: true, separator: ','));
pipeline.Add(new ColumnConcatenator("Features", "YearsExperience"));
pipeline.Add(new GeneralizedAdditiveModelRegressor());
#region
//call train method on the pipelien will train on the data it has
//object is generic derrived also
//two arguments - one is data input, next is dataoutput
//requires the data input in one column
#endregion
var model = pipeline.Train <SalaryData, SalaryPrediction>();
#region
//testing the effectiveness of the model
//RegressionEvaluator from Microsoft.ML.Models
//map to a newfile called SalaryData-test.csv which holds the test data set (not the cross validation data set)
//we can use this to stop overfitting / other errors
//RegressionEvaluator - call Evaluate method on this and it will give you... metrics to checkl teh effectiveness of the model
//the above method requires the model and test data passing in to check how effective it has been
//the best evaluator metrics are the root mean squared and the r squared values )
//RMS gives the average actual value our model was out by (it gives on average $4417)
//The R^2 vlaue is teh value I have been seeign in the course - how far the actual values in the test data are from the relative model data - the total error - closer to 1, teh better your model is... there are some problems with this value ( I am guessin potential overfitting closer to 1)
#endregion
var testData = new TextLoader("SalaryData-test.csv").CreateFrom <SalaryData>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"RMS - {metrics.Rms}");
Console.WriteLine($"R^2 - {metrics.RSquared}");
#region
//predict on unseen data
//call predict method on the model, pass in a new SalaryData model - set the Feature you want to predict on
#endregion
var prediction = model.Predict(new SalaryData {
YearsExperience = 7
});
Console.WriteLine($"Predicted Salary for 7 = {prediction.PredictedSalary}");
Console.ReadLine();
}
public void TrainAndPredictHousePriceModelTest()
{
string dataPath = GetDataPath("kc_house_data.csv");
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader <HousePriceData>(dataPath, useHeader: true, separator: ","));
pipeline.Add(new ColumnConcatenator(outputColumn: "NumericalFeatures",
"SqftLiving", "SqftLot", "SqftAbove", "SqftBasement", "Lat", "Long", "SqftLiving15", "SqftLot15"));
pipeline.Add(new ColumnConcatenator(outputColumn: "CategoryFeatures",
"Bedrooms", "Bathrooms", "Floors", "Waterfront", "View", "Condition", "Grade", "YearBuilt", "YearRenovated", "Zipcode"));
pipeline.Add(new CategoricalOneHotVectorizer("CategoryFeatures"));
pipeline.Add(new ColumnConcatenator(outputColumn: "Features",
"NumericalFeatures", "CategoryFeatures"));
pipeline.Add(new StochasticDualCoordinateAscentRegressor());
PredictionModel <HousePriceData, HousePricePrediction> model = pipeline.Train <HousePriceData, HousePricePrediction>();
HousePricePrediction prediction = model.Predict(new HousePriceData()
{
Bedrooms = 3,
Bathrooms = 2,
SqftLiving = 1710,
SqftLot = 4697,
Floors = 1.5f,
Waterfront = 0,
View = 0,
Condition = 5,
Grade = 6,
SqftAbove = 1710,
SqftBasement = 0,
YearBuilt = 1941,
YearRenovated = 0,
Zipcode = 98002,
Lat = 47.3048f,
Long = -122.218f,
SqftLiving15 = 1030,
SqftLot15 = 4705
});
Assert.InRange(prediction.Price, 260_000, 330_000);
string testDataPath = GetDataPath("kc_house_test.csv");
var testData = new TextLoader <HousePriceData>(testDataPath, useHeader: true, separator: ",");
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
Assert.InRange(metrics.L1, 85_000, 89_000);
Assert.InRange(metrics.L2, 17_000_000_000, 19_000_000_000);
Assert.InRange(metrics.Rms, 130_500, 135_000);
Assert.InRange(metrics.LossFn, 17_000_000_000, 19_000_000_000);
Assert.Equal(.8, metrics.RSquared, 1);
}
public static Task <RegressionMetrics> Evaluate(this PredictionModel <Sell, SellPrediction> model)
{
return(Task.Factory.StartNew(() =>
{
var dataLoader = new TextLoader(Files.LoadTestDataSet()).CreateFrom <Sell>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
return evaluator.Evaluate(model, dataLoader);
}));
}
/// <summary>
/// モデルを評価します。
/// </summary>
/// <param name="model"></param>
/// <param name="testDataPath"></param>
private void Evaluate(PredictionModel <BostonHousingData, BostonHousingPrediction> model, string testDataPath)
{
var testData = new TextLoader(testDataPath).CreateFrom <BostonHousingData>(true, ',');
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"Rms = {metrics.Rms}");
Console.WriteLine($"RSquared = {metrics.RSquared}");
}
private static void Evaluate(PredictionModel <PackageRelease, PackageReleasePrediction> model)
{
var testData = new TextLoader(_testdatapath).CreateFrom <PackageRelease>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"Rms = {metrics.Rms}");
}
private static void Evaluate(PredictionModel <Product, ProductSugars_100gPrediction> model)
{
var testData = new TextLoader(_testdatapath).CreateFrom <Product>(useHeader: true, separator: _separator);
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"Rms = {metrics.Rms}");
Console.WriteLine($"RSquared = {metrics.RSquared}");
}
public static void Evaluate(PredictionModel <TaxiTrip, TaxiTripFarePrediction> model)
{
var testData = new TextLoader(_testDataPath).CreateFrom <TaxiTrip>(separator: ',');
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"Rms = {metrics.Rms}");
Console.WriteLine($"Rsquared = {metrics.RSquared}");
}
private static void Evaluate(PredictionModel <TaxiTrip, TaxiTripFarePrediction> model)
{
var testData = new TextLoader <TaxiTrip>(_testDataPath, useHeader: true, separator: ",");
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
Console.WriteLine("Rms=" + metrics.Rms);
Console.WriteLine("RSquared = " + metrics.RSquared);
}
public static void Evaluate(PredictionModel <TaxiTrip, TaxiTripFarePrediction> model)
{
var testData = new TextLoader(_testDataPath).CreateFrom <TaxiTrip>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(model, testData);
Console.WriteLine("PredictionModel quality metrics evaluation");
Console.WriteLine($"Rms: {metrics.Rms}");
Console.WriteLine($"RSquared: {metrics.RSquared}");
}
private static void Evaluate(PredictionModel <NodeObject, NodePrediction> model)
{
TextLoader testData = new TextLoader(_testdatapath).CreateFrom <NodeObject>(useHeader: true, separator: ',');
RegressionEvaluator evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"Rms = {metrics.Rms}");
Console.WriteLine($"RSquared = {metrics.RSquared}");
}
private static void Evaluate(PredictionModel <ItemStock, itemStockQtyPrediction> model)
{
var testData = new TextLoader(_Evaluatedatapath).CreateFrom <ItemStock>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"Rms = {metrics.Rms}");
Console.WriteLine($"RSquared = {metrics.RSquared}");
}
public RegressionMamlEvaluator(IHostEnvironment env, Arguments args)
: base(args, env, MetadataUtils.Const.ScoreColumnKind.Regression, "RegressionMamlEvaluator")
{
Host.CheckUserArg(args.LossFunction != null, nameof(args.LossFunction), "Loss function must be specified.");
var evalArgs = new RegressionEvaluator.Arguments();
evalArgs.LossFunction = args.LossFunction;
_evaluator = new RegressionEvaluator(Host, evalArgs);
}
/// <summary>
/// Evaluates scored regression data.
/// </summary>
/// <param name="data">The scored data.</param>
/// <param name="labelColumnName">The name of the label column in <paramref name="data"/>.</param>
/// <param name="scoreColumnName">The name of the score column in <paramref name="data"/>.</param>
/// <returns>The evaluation results for these calibrated outputs.</returns>
public RegressionMetrics Evaluate(IDataView data, string labelColumnName = DefaultColumnNames.Label, string scoreColumnName = DefaultColumnNames.Score)
{
Environment.CheckValue(data, nameof(data));
Environment.CheckNonEmpty(labelColumnName, nameof(labelColumnName));
Environment.CheckNonEmpty(scoreColumnName, nameof(scoreColumnName));
var eval = new RegressionEvaluator(Environment, new RegressionEvaluator.Arguments()
{
});
return(eval.Evaluate(data, labelColumnName, scoreColumnName));
}
private static void Evaluate(PredictionModel <HousePrice, HousePricePredicted> model)
{
var _testdatapath = @"pp-monthly-update-new-version.csv";
var testData = new TextLoader(_testdatapath).CreateFrom <HousePrice>(useHeader: false, separator: ',');
var evaluator = new RegressionEvaluator()
{
LabelColumn = "Price"
};
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
Console.WriteLine(JsonConvert.SerializeObject(metrics, Formatting.Indented));
}
/// <summary>
/// Evaluates the scored recommendation data.
/// </summary>
/// <param name="data">The scored data.</param>
/// <param name="label">The name of the label column in <paramref name="data"/>.</param>
/// <param name="score">The name of the score column in <paramref name="data"/>.</param>
/// <returns>The evaluation results for these calibrated outputs.</returns>
public RegressionEvaluator.Result Evaluate(IDataView data, string label = DefaultColumnNames.Label, string score = DefaultColumnNames.Score)
{
Host.CheckValue(data, nameof(data));
Host.CheckNonEmpty(label, nameof(label));
Host.CheckNonEmpty(score, nameof(score));
var eval = new RegressionEvaluator(Host, new RegressionEvaluator.Arguments()
{
});
return(eval.Evaluate(data, label, score));
}
private static void Evaluate(PredictionModel <TaxiTrip, TaxiTripFarePrediction> model)
{
var testData = new TextLoader(_testdatapath).CreateFrom <TaxiTrip>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
// Rms should be around 2.795276
Console.WriteLine("Rms=" + metrics.Rms);
Console.WriteLine("RSquared = " + metrics.RSquared);
Console.WriteLine();
}
/// <summary>
/// 评估模型
/// </summary>
/// <param name="model"></param>
public static void Evaluate(PredictionModel <TaxiTrip, TaxiTripFarePrediction> model)
{
var testData = new TextLoader(TestDataPath).CreateFrom <TaxiTrip>(useHeader: true, separator: ',');
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
// Rms should be around 2.795276
//RMS是评估回归问题的一个指标。它越低，你的模型就越好。将以下代码添加到该Evaluate()函数中以打印模型的RMS。
Console.WriteLine("Rms=" + metrics.Rms);
//Squared是评估回归问题的另一个指标。RSquared将是介于0和1之间的值。越接近1，模型越好。将下面的代码添加到该Evaluate()函数中以打印模型的RSquared值。
Console.WriteLine("RSquared = " + metrics.RSquared);
}
public static void GetMyPrediction()
{
Console.WriteLine("Begin ML.NET demo run");
Console.WriteLine("Income from age, sex, politics");
var pipeline = new LearningPipeline();
string dataPath = AppDomain.CurrentDomain.BaseDirectory + "/datamodel/PeopleData.txt";
pipeline.Add(new TextLoader(dataPath).
CreateFrom <IncomeData>(separator: ','));
pipeline.Add(new ColumnCopier(("Income", "Label")));
pipeline.Add(new CategoricalOneHotVectorizer("Politic"));
pipeline.Add(new ColumnConcatenator("Features", "Age",
"Sex", "Politic"));
var sdcar = new StochasticDualCoordinateAscentRegressor();
sdcar.MaxIterations = 1000;
sdcar.NormalizeFeatures = NormalizeOption.Auto;
pipeline.Add(sdcar);
// pipeline.N
Console.WriteLine("\nStarting training \n");
var model = pipeline.Train <IncomeData, IncomePrediction>();
Console.WriteLine("\nTraining complete \n");
string modelPath = AppDomain.CurrentDomain.BaseDirectory + "/IncomeModel.zip";
Task.Run(async() =>
{
await model.WriteAsync(modelPath);
}).GetAwaiter().GetResult();
var testData = new TextLoader(dataPath).
CreateFrom <IncomeData>(separator: ',');
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(model, testData);
double rms = metrics.Rms;
Console.WriteLine("Root mean squared error = " +
rms.ToString("F4"));
Console.WriteLine("Income age 40 conservative male: ");
IncomeData newPatient = new IncomeData()
{
Age = 40.0f,
Sex = -1f,
Politic = "conservative"
};
IncomePrediction prediction = model.Predict(newPatient);
float predIncome = prediction.Income * 10000;
Console.WriteLine("Predicted income = $" +
predIncome.ToString("F2"));
Console.WriteLine("\nEnd ML.NET demo");
Console.ReadLine();
} // Main
private static void Evaluate(PredictionModel <TaxiTrip, TaxiTripFarePrediction> model)
{
var textData = new TextLoader <TaxiTrip>(TestDataPath, useHeader: true, separator: ",");
var evaluator = new RegressionEvaluator();
RegressionMetrics matrics = evaluator.Evaluate(model, textData);
Console.WriteLine("Rms=" + matrics.Rms);
Console.WriteLine("RSquared=" + matrics.RSquared);
TaxiTripFarePrediction prediction01 = model.Predict(TestTrips.Trip1);
Console.WriteLine("Predict fare : {0}, actual fare : 29.5", prediction01.fare_amount);
}
