StandardTrainersCatalog.SdcaNonCalibrated 方法
定義
重要
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多載
SdcaNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, SdcaNonCalibratedBinaryTrainer+Options)
SdcaNonCalibratedBinaryTrainer使用進階選項建立 ，其會使用透過布林值標籤資料定型的線性分類模型來預測目標。
public static Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer SdcaNonCalibrated (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer.Options options);static member SdcaNonCalibrated : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer.Options -> Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer<Extension()>
Public Function SdcaNonCalibrated (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, options As SdcaNonCalibratedBinaryTrainer.Options) As SdcaNonCalibratedBinaryTrainer參數
二元分類目錄定型器物件。
定型器選項。
傳回
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers;
namespace Samples.Dynamic.Trainers.BinaryClassification
{
public static class SdcaNonCalibratedWithOptions
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness. Setting the seed to a fixed number
// in this example to make outputs deterministic.
var mlContext = new MLContext(seed: 0);
// Create a list of training data points.
var dataPoints = GenerateRandomDataPoints(1000);
// Convert the list of data points to an IDataView object, which is
// consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
// ML.NET doesn't cache data set by default. Therefore, if one reads a
// data set from a file and accesses it many times, it can be slow due
// to expensive featurization and disk operations. When the considered
// data can fit into memory, a solution is to cache the data in memory.
// Caching is especially helpful when working with iterative algorithms
// which needs many data passes.
trainingData = mlContext.Data.Cache(trainingData);
// Define trainer options.
var options = new SdcaNonCalibratedBinaryTrainer.Options()
{
// Specify loss function.
LossFunction = new HingeLoss(),
// Make the convergence tolerance tighter.
ConvergenceTolerance = 0.05f,
// Increase the maximum number of passes over training data.
MaximumNumberOfIterations = 30,
// Give the instances of the positive class slightly more weight.
PositiveInstanceWeight = 1.2f,
};
// Define the trainer.
var pipeline = mlContext.BinaryClassification.Trainers
.SdcaNonCalibrated(options);
// Train the model.
var model = pipeline.Fit(trainingData);
// Create testing data. Use different random seed to make it different
// from training data.
var testData = mlContext.Data
.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
// Run the model on test data set.
var transformedTestData = model.Transform(testData);
// Convert IDataView object to a list.
var predictions = mlContext.Data
.CreateEnumerable<Prediction>(transformedTestData,
reuseRowObject: false).ToList();
// Print 5 predictions.
foreach (var p in predictions.Take(5))
Console.WriteLine($"Label: {p.Label}, "
+ $"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: True, Prediction: False
// Label: False, Prediction: False
// Label: True, Prediction: True
// Label: True, Prediction: True
// Label: False, Prediction: True
// Evaluate the overall metrics.
var metrics = mlContext.BinaryClassification
.EvaluateNonCalibrated(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.61
// AUC: 0.67
// F1 Score: 0.65
// Negative Precision: 0.69
// Negative Recall: 0.45
// Positive Precision: 0.56
// Positive Recall: 0.77
//
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 178 | 60 | 0.7479
// negative || 134 | 128 | 0.4885
// ||======================
// Precision || 0.5705 | 0.6809 |
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
for (int i = 0; i < count; i++)
{
var label = randomFloat() > 0.5f;
yield return new DataPoint
{
Label = label,
// Create random features that are correlated with the label.
// For data points with false label, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50)
.Select(x => x ? randomFloat() : randomFloat() +
0.03f).ToArray()
};
}
}
// Example with label and 50 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public bool Label { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public bool Label { get; set; }
// Predicted label from the trainer.
public bool PredictedLabel { get; set; }
}
// Pretty-print BinaryClassificationMetrics objects.
private static void PrintMetrics(BinaryClassificationMetrics metrics)
{
Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}");
Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}");
Console.WriteLine($"F1 Score: {metrics.F1Score:F2}");
Console.WriteLine($"Negative Precision: " +
$"{metrics.NegativePrecision:F2}");
Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}");
Console.WriteLine($"Positive Precision: " +
$"{metrics.PositivePrecision:F2}");
Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n");
Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
}
}
}
適用於
SdcaNonCalibrated(MulticlassClassificationCatalog+MulticlassClassificationTrainers, SdcaNonCalibratedMulticlassTrainer+Options)
SdcaNonCalibratedMulticlassTrainer使用進階選項建立 ，其會使用以座標下降方法定型的線性多類別分類模型來預測目標。
public static Microsoft.ML.Trainers.SdcaNonCalibratedMulticlassTrainer SdcaNonCalibrated (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, Microsoft.ML.Trainers.SdcaNonCalibratedMulticlassTrainer.Options options);static member SdcaNonCalibrated : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * Microsoft.ML.Trainers.SdcaNonCalibratedMulticlassTrainer.Options -> Microsoft.ML.Trainers.SdcaNonCalibratedMulticlassTrainer<Extension()>
Public Function SdcaNonCalibrated (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, options As SdcaNonCalibratedMulticlassTrainer.Options) As SdcaNonCalibratedMulticlassTrainer參數
多類別分類目錄定型器物件。
定型器選項。
傳回
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers;
namespace Samples.Dynamic.Trainers.MulticlassClassification
{
public static class SdcaNonCalibratedWithOptions
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness. Setting the seed to a fixed number
// in this example to make outputs deterministic.
var mlContext = new MLContext(seed: 0);
// Create a list of training data points.
var dataPoints = GenerateRandomDataPoints(1000);
// Convert the list of data points to an IDataView object, which is
// consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
// ML.NET doesn't cache data set by default. Therefore, if one reads a
// data set from a file and accesses it many times, it can be slow due
// to expensive featurization and disk operations. When the considered
// data can fit into memory, a solution is to cache the data in memory.
// Caching is especially helpful when working with iterative algorithms
// which needs many data passes.
trainingData = mlContext.Data.Cache(trainingData);
// Define trainer options.
var options = new SdcaNonCalibratedMulticlassTrainer.Options
{
Loss = new HingeLoss(),
L1Regularization = 0.1f,
BiasLearningRate = 0.01f,
NumberOfThreads = 1
};
// Define the trainer.
var pipeline =
// Convert the string labels into key types.
mlContext.Transforms.Conversion.MapValueToKey("Label")
// Apply SdcaNonCalibrated multiclass trainer.
.Append(mlContext.MulticlassClassification.Trainers
.SdcaNonCalibrated(options));
// Train the model.
var model = pipeline.Fit(trainingData);
// Create testing data. Use different random seed to make it different
// from training data.
var testData = mlContext.Data
.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
// Run the model on test data set.
var transformedTestData = model.Transform(testData);
// Convert IDataView object to a list.
var predictions = mlContext.Data
.CreateEnumerable<Prediction>(transformedTestData,
reuseRowObject: false).ToList();
// Look at 5 predictions
foreach (var p in predictions.Take(5))
Console.WriteLine($"Label: {p.Label}, " +
$"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: 1, Prediction: 1
// Label: 2, Prediction: 2
// Label: 3, Prediction: 2
// Label: 2, Prediction: 2
// Label: 3, Prediction: 3
// Evaluate the overall metrics
var metrics = mlContext.MulticlassClassification
.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Micro Accuracy: 0.91
// Macro Accuracy: 0.91
// Log Loss: 0.22
// Log Loss Reduction: 0.80
// Confusion table
// ||========================
// PREDICTED || 0 | 1 | 2 | Recall
// TRUTH ||========================
// 0 || 145 | 0 | 15 | 0.9063
// 1 || 0 | 164 | 13 | 0.9266
// 2 || 12 | 7 | 144 | 0.8834
// ||========================
// Precision ||0.9236 |0.9591 |0.8372 |
}
// Generates random uniform doubles in [-0.5, 0.5)
// range with labels 1, 2 or 3.
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)(random.NextDouble() - 0.5);
for (int i = 0; i < count; i++)
{
// Generate Labels that are integers 1, 2 or 3
var label = random.Next(1, 4);
yield return new DataPoint
{
Label = (uint)label,
// Create random features that are correlated with the label.
// The feature values are slightly increased by adding a
// constant multiple of label.
Features = Enumerable.Repeat(label, 20)
.Select(x => randomFloat() + label * 0.2f).ToArray()
};
}
}
// Example with label and 20 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public uint Label { get; set; }
[VectorType(20)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public uint Label { get; set; }
// Predicted label from the trainer.
public uint PredictedLabel { get; set; }
}
// Pretty-print MulticlassClassificationMetrics objects.
public static void PrintMetrics(MulticlassClassificationMetrics metrics)
{
Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
Console.WriteLine(
$"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
}
}
}
適用於
SdcaNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, ISupportSdcaClassificationLoss, Nullable<Single>, Nullable<Single>, Nullable<Int32>)
建立 SdcaNonCalibratedBinaryTrainer ，其會使用線性分類模型預測目標。
public static Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer SdcaNonCalibrated (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, Microsoft.ML.Trainers.ISupportSdcaClassificationLoss lossFunction = default, float? l2Regularization = default, float? l1Regularization = default, int? maximumNumberOfIterations = default);static member SdcaNonCalibrated : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * string * Microsoft.ML.Trainers.ISupportSdcaClassificationLoss * Nullable<single> * Nullable<single> * Nullable<int> -> Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer<Extension()>
Public Function SdcaNonCalibrated (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional lossFunction As ISupportSdcaClassificationLoss = Nothing, Optional l2Regularization As Nullable(Of Single) = Nothing, Optional l1Regularization As Nullable(Of Single) = Nothing, Optional maximumNumberOfIterations As Nullable(Of Integer) = Nothing) As SdcaNonCalibratedBinaryTrainer參數
二元分類目錄定型器物件。
- exampleWeightColumnName
- String
範例加權資料行的名稱 (選擇性) 。
- lossFunction
- ISupportSdcaClassificationLoss
傳回
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic.Trainers.BinaryClassification
{
public static class SdcaNonCalibrated
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness. Setting the seed to a fixed number
// in this example to make outputs deterministic.
var mlContext = new MLContext(seed: 0);
// Create a list of training data points.
var dataPoints = GenerateRandomDataPoints(1000);
// Convert the list of data points to an IDataView object, which is
// consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
// ML.NET doesn't cache data set by default. Therefore, if one reads a
// data set from a file and accesses it many times, it can be slow due
// to expensive featurization and disk operations. When the considered
// data can fit into memory, a solution is to cache the data in memory.
// Caching is especially helpful when working with iterative algorithms
// which needs many data passes.
trainingData = mlContext.Data.Cache(trainingData);
// Define the trainer.
var pipeline = mlContext.BinaryClassification.Trainers
.SdcaNonCalibrated();
// Train the model.
var model = pipeline.Fit(trainingData);
// Create testing data. Use different random seed to make it different
// from training data.
var testData = mlContext.Data
.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
// Run the model on test data set.
var transformedTestData = model.Transform(testData);
// Convert IDataView object to a list.
var predictions = mlContext.Data
.CreateEnumerable<Prediction>(transformedTestData,
reuseRowObject: false).ToList();
// Print 5 predictions.
foreach (var p in predictions.Take(5))
Console.WriteLine($"Label: {p.Label}, "
+ $"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: True, Prediction: True
// Label: False, Prediction: True
// Label: True, Prediction: True
// Label: True, Prediction: True
// Label: False, Prediction: True
// Evaluate the overall metrics.
var metrics = mlContext.BinaryClassification
.EvaluateNonCalibrated(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.65
// AUC: 0.69
// F1 Score: 0.64
// Negative Precision: 0.68
// Negative Recall: 0.65
// Positive Precision: 0.63
// Positive Recall: 0.66
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 154 | 84 | 0.6471
// negative || 95 | 167 | 0.6374
// ||======================
// Precision || 0.6185 | 0.6653 |
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
for (int i = 0; i < count; i++)
{
var label = randomFloat() > 0.5f;
yield return new DataPoint
{
Label = label,
// Create random features that are correlated with the label.
// For data points with false label, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50)
.Select(x => x ? randomFloat() : randomFloat() +
0.03f).ToArray()
};
}
}
// Example with label and 50 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public bool Label { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public bool Label { get; set; }
// Predicted label from the trainer.
public bool PredictedLabel { get; set; }
}
// Pretty-print BinaryClassificationMetrics objects.
private static void PrintMetrics(BinaryClassificationMetrics metrics)
{
Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}");
Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}");
Console.WriteLine($"F1 Score: {metrics.F1Score:F2}");
Console.WriteLine($"Negative Precision: " +
$"{metrics.NegativePrecision:F2}");
Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}");
Console.WriteLine($"Positive Precision: " +
$"{metrics.PositivePrecision:F2}");
Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n");
Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
}
}
}
適用於
SdcaNonCalibrated(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, ISupportSdcaClassificationLoss, Nullable<Single>, Nullable<Single>, Nullable<Int32>)
建立 SdcaNonCalibratedMulticlassTrainer ，其會使用以座標下降方法定型的線性多類別分類模型來預測目標。
public static Microsoft.ML.Trainers.SdcaNonCalibratedMulticlassTrainer SdcaNonCalibrated (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, Microsoft.ML.Trainers.ISupportSdcaClassificationLoss lossFunction = default, float? l2Regularization = default, float? l1Regularization = default, int? maximumNumberOfIterations = default);static member SdcaNonCalibrated : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * string * string * string * Microsoft.ML.Trainers.ISupportSdcaClassificationLoss * Nullable<single> * Nullable<single> * Nullable<int> -> Microsoft.ML.Trainers.SdcaNonCalibratedMulticlassTrainer<Extension()>
Public Function SdcaNonCalibrated (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional lossFunction As ISupportSdcaClassificationLoss = Nothing, Optional l2Regularization As Nullable(Of Single) = Nothing, Optional l1Regularization As Nullable(Of Single) = Nothing, Optional maximumNumberOfIterations As Nullable(Of Integer) = Nothing) As SdcaNonCalibratedMulticlassTrainer參數
多類別分類目錄定型器物件。
- labelColumnName
- String
標籤資料行的名稱。 資料行資料必須是 KeyDataViewType 。
- exampleWeightColumnName
- String
範例加權資料行的名稱 (選擇性) 。
- lossFunction
- ISupportSdcaClassificationLoss
傳回
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic.Trainers.MulticlassClassification
{
public static class SdcaNonCalibrated
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness. Setting the seed to a fixed number
// in this example to make outputs deterministic.
var mlContext = new MLContext(seed: 0);
// Create a list of training data points.
var dataPoints = GenerateRandomDataPoints(1000);
// Convert the list of data points to an IDataView object, which is
// consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
// ML.NET doesn't cache data set by default. Therefore, if one reads a
// data set from a file and accesses it many times, it can be slow due
// to expensive featurization and disk operations. When the considered
// data can fit into memory, a solution is to cache the data in memory.
// Caching is especially helpful when working with iterative algorithms
// which needs many data passes.
trainingData = mlContext.Data.Cache(trainingData);
// Define the trainer.
var pipeline =
// Convert the string labels into key types.
mlContext.Transforms.Conversion
.MapValueToKey(nameof(DataPoint.Label))
// Apply SdcaNonCalibrated multiclass trainer.
.Append(mlContext.MulticlassClassification.Trainers
.SdcaNonCalibrated());
// Train the model.
var model = pipeline.Fit(trainingData);
// Create testing data. Use different random seed to make it different
// from training data.
var testData = mlContext.Data
.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
// Run the model on test data set.
var transformedTestData = model.Transform(testData);
// Convert IDataView object to a list.
var predictions = mlContext.Data
.CreateEnumerable<Prediction>(transformedTestData,
reuseRowObject: false).ToList();
// Look at 5 predictions
foreach (var p in predictions.Take(5))
Console.WriteLine($"Label: {p.Label}, " +
$"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: 1, Prediction: 1
// Label: 2, Prediction: 2
// Label: 3, Prediction: 2
// Label: 2, Prediction: 2
// Label: 3, Prediction: 3
// Evaluate the overall metrics
var metrics = mlContext.MulticlassClassification
.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Micro Accuracy: 0.91
// Macro Accuracy: 0.91
// Log Loss: 0.57
// Log Loss Reduction: 0.48
// Confusion table
// ||========================
// PREDICTED || 0 | 1 | 2 | Recall
// TRUTH ||========================
// 0 || 147 | 0 | 13 | 0.9188
// 1 || 0 | 165 | 12 | 0.9322
// 2 || 11 | 8 | 144 | 0.8834
// ||========================
// Precision ||0.9304 |0.9538 |0.8521 |
}
// Generates random uniform doubles in [-0.5, 0.5)
// range with labels 1, 2 or 3.
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)(random.NextDouble() - 0.5);
for (int i = 0; i < count; i++)
{
// Generate Labels that are integers 1, 2 or 3
var label = random.Next(1, 4);
yield return new DataPoint
{
Label = (uint)label,
// Create random features that are correlated with the label.
// The feature values are slightly increased by adding a
// constant multiple of label.
Features = Enumerable.Repeat(label, 20)
.Select(x => randomFloat() + label * 0.2f).ToArray()
};
}
}
// Example with label and 20 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public uint Label { get; set; }
[VectorType(20)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public uint Label { get; set; }
// Predicted label from the trainer.
public uint PredictedLabel { get; set; }
}
// Pretty-print MulticlassClassificationMetrics objects.
public static void PrintMetrics(MulticlassClassificationMetrics metrics)
{
Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
Console.WriteLine(
$"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
}
}
}