ML.Net has the capability of utilizing 7 different Machine Learning Tasks via the MLContext object:
- Binary Classification
- Multi-class/text Classification
- Regression and Recommendation
- Clustering
- Ranking
- Anomaly Detection
- sentence similarity
Each task offers various performance metrics for evaluating the model after training is completed
These metrics are properties accessible via the Evaluate() method within each task object (i.e. MLContext.MLTask.Evaluate()
Sample Code Snippet
static void Main(string[] args)
{
MLContext mlContext = new MLContext();
// 1a. Create training data
HouseData[] houseData = {
new HouseData() { Size = 1.1F, Price = 1.2F },
new HouseData() { Size = 1.9F, Price = 2.3F },
new HouseData() { Size = 2.8F, Price = 3.0F },
new HouseData() { Size = 3.4F, Price = 3.7F } };
// 1b. Import training data
IDataView trainingData = mlContext.Data.LoadFromEnumerable(houseData);
// 2. Specify data preparation and model training pipeline
var pipeline = mlContext.Transforms.Concatenate("Features", new[] { "Size" })
.Append(mlContext.Regression.Trainers.Sdca(labelColumnName: "Price", maximumNumberOfIterations: 100));
// 3. Train model
var model = pipeline.Fit(trainingData);
//***** Model Evaluation
HouseData[] testHouseData =
{
new HouseData() { Size = 1.1F, Price = 0.98F },
new HouseData() { Size = 1.9F, Price = 2.1F },
new HouseData() { Size = 2.8F, Price = 2.9F },
new HouseData() { Size = 3.4F, Price = 3.6F }
};
var testHouseDataView = mlContext.Data.LoadFromEnumerable(testHouseData);
var testPriceDataView = model.Transform(testHouseDataView);
var metrics = mlContext.Regression.Evaluate(testPriceDataView, labelColumnName: "Price");
double rs = metrics.RSquared;
double rmse = metrics.RootMeanSquaredError;
}
Metrics Summary
Listed below is a summary of 6 various ML.NET Tasks and their metrics:
| BinaryClassification | MulticlassClassification | Regression |
| MAE (Mean Absolute Error) | ||
| AreaUnderPrecisionRecallCurve | Log-Loss | MSE (Mean Squared Error) |
| RMSE (Root Mean Square Error) |
| Clustering | Ranking | AnomalyDetection |
| Avg Distance | DCG | Area Under ROC Curve |
| Davies Boulding Index | Normalized DCG | Detection Rate At False Positive Count |
| NMI | | |
Reference: https://learn.microsoft.com/en-us/dotnet/machine-learning/resources/metrics
