PermutationFeatureImportanceExtensions.PermutationFeatureImportance 方法
定義
重要
部分資訊涉及發行前產品，在發行之前可能會有大幅修改。 Microsoft 對此處提供的資訊，不做任何明確或隱含的瑕疵擔保。
多載
PermutationFeatureImportance(MulticlassClassificationCatalog, ITransformer, IDataView, String, Boolean, Nullable<Int32>, Int32)
多重類別化 (PFI) 的排列特徵重要性。
public static System.Collections.Immutable.ImmutableDictionary<string,Microsoft.ML.Data.MulticlassClassificationMetricsStatistics> PermutationFeatureImportance (this Microsoft.ML.MulticlassClassificationCatalog catalog, Microsoft.ML.ITransformer model, Microsoft.ML.IDataView data, string labelColumnName = "Label", bool useFeatureWeightFilter = false, int? numberOfExamplesToUse = default, int permutationCount = 1);static member PermutationFeatureImportance : Microsoft.ML.MulticlassClassificationCatalog * Microsoft.ML.ITransformer * Microsoft.ML.IDataView * string * bool * Nullable<int> * int -> System.Collections.Immutable.ImmutableDictionary<string, Microsoft.ML.Data.MulticlassClassificationMetricsStatistics><Extension()>
Public Function PermutationFeatureImportance (catalog As MulticlassClassificationCatalog, model As ITransformer, data As IDataView, Optional labelColumnName As String = "Label", Optional useFeatureWeightFilter As Boolean = false, Optional numberOfExamplesToUse As Nullable(Of Integer) = Nothing, Optional permutationCount As Integer = 1) As ImmutableDictionary(Of String, MulticlassClassificationMetricsStatistics)參數
- catalog
- MulticlassClassificationCatalog
多類別分類目錄。
- model
- ITransformer
要評估特徵重要性的模型。
- data
- IDataView
評估資料集。
- labelColumnName
- String
標籤資料行名稱。 資料行資料必須是 KeyDataViewType 。
- useFeatureWeightFilter
- Boolean
使用功能權數預先篩選功能。
- permutationCount
- Int32
要執行的排列數目。
傳回
字典會將每個功能對應至分數的每個功能「貢獻」。
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.MulticlassClassification
{
public static class PermutationFeatureImportance
{
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.
var mlContext = new MLContext(seed: 1);
// Create sample data.
var samples = GenerateData();
// Load the sample data as an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Define a training pipeline that concatenates features into a vector,
// normalizes them, and then trains a linear model.
var featureColumns =
new string[] { nameof(Data.Feature1), nameof(Data.Feature2) };
var pipeline = mlContext.Transforms
.Concatenate("Features", featureColumns)
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.MulticlassClassification.Trainers
.SdcaMaximumEntropy());
// Fit the pipeline to the data.
var model = pipeline.Fit(data);
// Transform the dataset.
var transformedData = model.Transform(data);
// Extract the predictor.
var linearPredictor = model.LastTransformer;
// Compute the permutation metrics for the linear model using the
// normalized data.
var permutationMetrics = mlContext.MulticlassClassification
.PermutationFeatureImportance(linearPredictor, transformedData,
permutationCount: 30);
// Now let's look at which features are most important to the model
// overall. Get the feature indices sorted by their impact on
// microaccuracy.
var sortedIndices = permutationMetrics
.Select((metrics, index) => new { index, metrics.MicroAccuracy })
.OrderByDescending(feature => Math.Abs(feature.MicroAccuracy.Mean))
.Select(feature => feature.index);
Console.WriteLine("Feature\tChange in MicroAccuracy\t95% Confidence in "
+ "the Mean Change in MicroAccuracy");
var microAccuracy = permutationMetrics.Select(x => x.MicroAccuracy)
.ToArray();
foreach (int i in sortedIndices)
{
Console.WriteLine("{0}\t{1:G4}\t{2:G4}",
featureColumns[i],
microAccuracy[i].Mean,
1.96 * microAccuracy[i].StandardError);
}
// Expected output:
//Feature Change in MicroAccuracy 95% Confidence in the Mean Change in MicroAccuracy
//Feature2 -0.1395 0.0006567
//Feature1 -0.05367 0.0006908
}
private class Data
{
public float Label { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
/// <summary>
/// Generate an enumerable of Data objects, creating the label as a simple
/// linear combination of the features.
/// </summary>
/// <param name="nExamples">The number of examples.</param>
/// <param name="bias">The bias, or offset, in the calculation of the
/// label.</param>
/// <param name="weight1">The weight to multiply the first feature with to
/// compute the label.</param>
/// <param name="weight2">The weight to multiply the second feature with to
/// compute the label.</param>
/// <param name="seed">The seed for generating feature values and label
/// noise.</param>
/// <returns>An enumerable of Data objects.</returns>
private static IEnumerable<Data> GenerateData(int nExamples = 10000,
double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1)
{
var rng = new Random(seed);
var max = bias + 4.5 * weight1 + 4.5 * weight2 + 0.5;
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
var value = (float)
(bias + weight1 * data.Feature1 + weight2 * data.Feature2 +
rng.NextDouble() - 0.5);
if (value < max / 3)
data.Label = 0;
else if (value < 2 * max / 3)
data.Label = 1;
else
data.Label = 2;
yield return data;
}
}
}
}
備註
PFI) (排列特徵重要性是判斷定型機器學習模型中特徵的全域重要性的技術。 PFI 是 Breiman 在隨機樹系檔中，第 10 節 (Breiman 所動機的簡單但功能強大的技術。 「隨機樹系」。 Machine Learning，2001.) PFI 方法的優點是其與模型無關，它可與任何可評估的模型搭配使用，而且可以使用任何資料集，而不只是定型集來計算特徵重要性計量。
PFI 的運作方式是採用標示的資料集、選擇特徵，並將該功能的值分散到所有範例中，讓每個範例現在都有特徵的隨機值，以及所有其他特徵的原始值。 評估計量 (例如，然後計算此修改資料集的微精確度) ，並計算來自原始資料集的評估計量變更。 評估計量中的變更愈大，功能對模型而言就越重要。 PFI 的運作方式是跨模型的所有功能，逐一執行此排列分析。
在此實作中，PFI 會計算每個功能的所有可能多重類別分類評估計量變更，並 ImmutableArray 傳回 物件的 MulticlassClassificationMetrics 。 如需使用這些結果來分析模型特徵重要性的範例，請參閱下列範例。
適用於
PermutationFeatureImportance(RegressionCatalog, ITransformer, IDataView, String, Boolean, Nullable<Int32>, Int32)
回歸的 PFI) (排列特徵重要性。
public static System.Collections.Immutable.ImmutableDictionary<string,Microsoft.ML.Data.RegressionMetricsStatistics> PermutationFeatureImportance (this Microsoft.ML.RegressionCatalog catalog, Microsoft.ML.ITransformer model, Microsoft.ML.IDataView data, string labelColumnName = "Label", bool useFeatureWeightFilter = false, int? numberOfExamplesToUse = default, int permutationCount = 1);static member PermutationFeatureImportance : Microsoft.ML.RegressionCatalog * Microsoft.ML.ITransformer * Microsoft.ML.IDataView * string * bool * Nullable<int> * int -> System.Collections.Immutable.ImmutableDictionary<string, Microsoft.ML.Data.RegressionMetricsStatistics><Extension()>
Public Function PermutationFeatureImportance (catalog As RegressionCatalog, model As ITransformer, data As IDataView, Optional labelColumnName As String = "Label", Optional useFeatureWeightFilter As Boolean = false, Optional numberOfExamplesToUse As Nullable(Of Integer) = Nothing, Optional permutationCount As Integer = 1) As ImmutableDictionary(Of String, RegressionMetricsStatistics)參數
- catalog
- RegressionCatalog
回歸目錄。
- model
- ITransformer
要評估特徵重要性的模型。
- data
- IDataView
評估資料集。
- useFeatureWeightFilter
- Boolean
使用功能權數預先篩選功能。
- permutationCount
- Int32
要執行的排列數目。
傳回
字典會將每個功能對應至分數的每個功能「貢獻」。
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.Regression
{
public static class PermutationFeatureImportance
{
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.
var mlContext = new MLContext(seed: 1);
// Create sample data.
var samples = GenerateData();
// Load the sample data as an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Define a training pipeline that concatenates features into a vector,
// normalizes them, and then trains a linear model.
var featureColumns = new string[] { nameof(Data.Feature1),
nameof(Data.Feature2) };
var pipeline = mlContext.Transforms.Concatenate(
"Features",
featureColumns)
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.Regression.Trainers.Ols());
// Fit the pipeline to the data.
var model = pipeline.Fit(data);
// Transform the dataset.
var transformedData = model.Transform(data);
// Extract the predictor.
var linearPredictor = model.LastTransformer;
// Compute the permutation metrics for the linear model using the
// normalized data.
var permutationMetrics = mlContext.Regression
.PermutationFeatureImportance(
linearPredictor, transformedData, permutationCount: 30);
// Now let's look at which features are most important to the model
// overall. Get the feature indices sorted by their impact on RMSE.
var sortedIndices = permutationMetrics
.Select((metrics, index) => new
{
index,
metrics.RootMeanSquaredError
})
.OrderByDescending(feature => Math.Abs(
feature.RootMeanSquaredError.Mean))
.Select(feature => feature.index);
Console.WriteLine("Feature\tModel Weight\tChange in RMSE\t95%" +
"Confidence in the Mean Change in RMSE");
var rmse = permutationMetrics.Select(x => x.RootMeanSquaredError)
.ToArray();
foreach (int i in sortedIndices)
{
Console.WriteLine("{0}\t{1:0.00}\t{2:G4}\t{3:G4}",
featureColumns[i],
linearPredictor.Model.Weights[i],
rmse[i].Mean,
1.96 * rmse[i].StandardError);
}
// Expected output:
// Feature Model Weight Change in RMSE 95% Confidence in the Mean Change in RMSE
// Feature2 9.00 4.009 0.008304
// Feature1 4.48 1.901 0.003351
}
private class Data
{
public float Label { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
/// <summary>
/// Generate an enumerable of Data objects, creating the label as a simple
/// linear combination of the features.
/// </summary>
/// <param name="nExamples">The number of examples.</param>
/// <param name="bias">The bias, or offset, in the calculation of the label.
/// </param>
/// <param name="weight1">The weight to multiply the first feature with to
/// compute the label.</param>
/// <param name="weight2">The weight to multiply the second feature with to
/// compute the label.</param>
/// <param name="seed">The seed for generating feature values and label
/// noise.</param>
/// <returns>An enumerable of Data objects.</returns>
private static IEnumerable<Data> GenerateData(int nExamples = 10000,
double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1)
{
var rng = new Random(seed);
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
data.Label = (float)(bias + weight1 * data.Feature1 + weight2 *
data.Feature2 + rng.NextDouble() - 0.5);
yield return data;
}
}
}
}
備註
PFI) (排列特徵重要性是判斷定型機器學習模型中特徵的全域重要性的技術。 PFI 是 Breiman 在隨機樹系檔中，第 10 節 (Breiman 所動機的簡單但功能強大的技術。 「隨機樹系」。 Machine Learning，2001.) PFI 方法的優點是其與模型無關，它可與任何可評估的模型搭配使用，而且可以使用任何資料集，而不只是定型集來計算特徵重要性計量。
PFI 的運作方式是採用標示的資料集、選擇特徵，並將該功能的值分散到所有範例中，讓每個範例現在都有特徵的隨機值，以及所有其他特徵的原始值。 評估計量 (例如 R 平方) ，然後計算此修改資料集的評估計量變更，並計算原始資料集中的評估計量變更。 評估計量中的變更愈大，功能對模型而言就越重要。 PFI 的運作方式是跨模型的所有功能，逐一執行此排列分析。
在此實作中，PFI 會計算每個功能的所有可能回歸評估計量變更，並 ImmutableArray 傳回 物件的 RegressionMetrics 。 如需使用這些結果來分析模型特徵重要性的範例，請參閱下列範例。
適用於
PermutationFeatureImportance(RankingCatalog, ITransformer, IDataView, String, String, Boolean, Nullable<Int32>, Int32)
排名的 PFI) (排列特徵重要性。
public static System.Collections.Immutable.ImmutableDictionary<string,Microsoft.ML.Data.RankingMetricsStatistics> PermutationFeatureImportance (this Microsoft.ML.RankingCatalog catalog, Microsoft.ML.ITransformer model, Microsoft.ML.IDataView data, string labelColumnName = "Label", string rowGroupColumnName = "GroupId", bool useFeatureWeightFilter = false, int? numberOfExamplesToUse = default, int permutationCount = 1);static member PermutationFeatureImportance : Microsoft.ML.RankingCatalog * Microsoft.ML.ITransformer * Microsoft.ML.IDataView * string * string * bool * Nullable<int> * int -> System.Collections.Immutable.ImmutableDictionary<string, Microsoft.ML.Data.RankingMetricsStatistics><Extension()>
Public Function PermutationFeatureImportance (catalog As RankingCatalog, model As ITransformer, data As IDataView, Optional labelColumnName As String = "Label", Optional rowGroupColumnName As String = "GroupId", Optional useFeatureWeightFilter As Boolean = false, Optional numberOfExamplesToUse As Nullable(Of Integer) = Nothing, Optional permutationCount As Integer = 1) As ImmutableDictionary(Of String, RankingMetricsStatistics)參數
- catalog
- RankingCatalog
排名目錄。
- model
- ITransformer
要評估特徵重要性的模型。
- data
- IDataView
評估資料集。
- labelColumnName
- String
標籤資料行名稱。 資料行資料必須是 Single 或 KeyDataViewType 。
- rowGroupColumnName
- String
GroupId 資料行名稱
- useFeatureWeightFilter
- Boolean
使用功能權數預先篩選功能。
- permutationCount
- Int32
要執行的排列數目。
傳回
字典會將每個功能對應至分數的每個功能「貢獻」。
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.Ranking
{
public static class PermutationFeatureImportance
{
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.
var mlContext = new MLContext(seed: 1);
// Create sample data.
var samples = GenerateData();
// Load the sample data as an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Define a training pipeline that concatenates features into a vector,
// normalizes them, and then trains a linear model.
var featureColumns = new string[] { nameof(Data.Feature1), nameof(
Data.Feature2) };
var pipeline = mlContext.Transforms.Concatenate("Features",
featureColumns)
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
.Append(mlContext.Transforms.Conversion.MapValueToKey(
"GroupId"))
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.Ranking.Trainers.FastTree());
// Fit the pipeline to the data.
var model = pipeline.Fit(data);
// Transform the dataset.
var transformedData = model.Transform(data);
// Extract the predictor.
var linearPredictor = model.LastTransformer;
// Compute the permutation metrics for the linear model using the
// normalized data.
var permutationMetrics = mlContext.Ranking.PermutationFeatureImportance(
linearPredictor, transformedData, permutationCount: 30);
// Now let's look at which features are most important to the model
// overall. Get the feature indices sorted by their impact on NDCG@1.
var sortedIndices = permutationMetrics.Select((metrics, index) => new
{
index,
metrics.NormalizedDiscountedCumulativeGains
})
.OrderByDescending(feature => Math.Abs(
feature.NormalizedDiscountedCumulativeGains[0].Mean))
.Select(feature => feature.index);
Console.WriteLine("Feature\tChange in NDCG@1\t95% Confidence in the" +
"Mean Change in NDCG@1");
var ndcg = permutationMetrics.Select(
x => x.NormalizedDiscountedCumulativeGains).ToArray();
foreach (int i in sortedIndices)
{
Console.WriteLine("{0}\t{1:G4}\t{2:G4}",
featureColumns[i],
ndcg[i][0].Mean,
1.96 * ndcg[i][0].StandardError);
}
// Expected output:
// Feature Change in NDCG@1 95% Confidence in the Mean Change in NDCG@1
// Feature2 -0.2421 0.001748
// Feature1 -0.0513 0.001184
}
private class Data
{
public float Label { get; set; }
public int GroupId { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
/// <summary>
/// Generate an enumerable of Data objects, creating the label as a simple
/// linear combination of the features.
/// </summary>
///
/// <param name="nExamples">The number of examples.</param>
///
/// <param name="bias">The bias, or offset, in the calculation of the label.
/// </param>
///
/// <param name="weight1">The weight to multiply the first feature with to
/// compute the label.</param>
///
/// <param name="weight2">The weight to multiply the second feature with to
/// compute the label.</param>
///
/// <param name="seed">The seed for generating feature values and label
/// noise.</param>
///
/// <returns>An enumerable of Data objects.</returns>
private static IEnumerable<Data> GenerateData(int nExamples = 10000,
double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1,
int groupSize = 5)
{
var rng = new Random(seed);
var max = bias + 4.5 * weight1 + 4.5 * weight2 + 0.5;
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
GroupId = i / groupSize,
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
var value = (float)(bias + weight1 * data.Feature1 + weight2 *
data.Feature2 + rng.NextDouble() - 0.5);
if (value < max / 3)
data.Label = 0;
else if (value < 2 * max / 3)
data.Label = 1;
else
data.Label = 2;
yield return data;
}
}
}
}
備註
PFI) (排列特徵重要性是判斷定型機器學習模型中特徵的全域重要性的技術。 PFI 是 Breiman 在隨機樹系檔中，第 10 節 (Breiman 所動機的簡單但功能強大的技術。 「隨機樹系」。 Machine Learning，2001.) PFI 方法的優點是其與模型無關，它可與任何可評估的模型搭配使用，而且可以使用任何資料集，而不只是定型集來計算特徵重要性計量。
PFI 的運作方式是採用標示的資料集、選擇特徵，並將該功能的值分散到所有範例中，讓每個範例現在都有特徵的隨機值，以及所有其他特徵的原始值。 評估計量 (例如，然後計算此修改資料集的 NDCG) ，並計算來自原始資料集的評估計量變更。 評估計量中的變更愈大，功能對模型而言就越重要。 PFI 的運作方式是跨模型的所有功能，逐一執行此排列分析。
在此實作中，PFI 會計算每個功能的所有可能排名評估計量變更，並 ImmutableArray 傳回 物件的 RankingMetrics 。 如需使用這些結果來分析模型特徵重要性的範例，請參閱下列範例。
適用於
PermutationFeatureImportance<TModel>(BinaryClassificationCatalog, ISingleFeaturePredictionTransformer<TModel>, IDataView, String, Boolean, Nullable<Int32>, Int32)
二元分類的 PFI) (排列特徵重要性。
public static System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.BinaryClassificationMetricsStatistics> PermutationFeatureImportance<TModel> (this Microsoft.ML.BinaryClassificationCatalog catalog, Microsoft.ML.ISingleFeaturePredictionTransformer<TModel> predictionTransformer, Microsoft.ML.IDataView data, string labelColumnName = "Label", bool useFeatureWeightFilter = false, int? numberOfExamplesToUse = default, int permutationCount = 1) where TModel : class;static member PermutationFeatureImportance : Microsoft.ML.BinaryClassificationCatalog * Microsoft.ML.ISingleFeaturePredictionTransformer<'Model (requires 'Model : null)> * Microsoft.ML.IDataView * string * bool * Nullable<int> * int -> System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.BinaryClassificationMetricsStatistics> (requires 'Model : null)<Extension()>
Public Function PermutationFeatureImportance(Of TModel As Class) (catalog As BinaryClassificationCatalog, predictionTransformer As ISingleFeaturePredictionTransformer(Of TModel), data As IDataView, Optional labelColumnName As String = "Label", Optional useFeatureWeightFilter As Boolean = false, Optional numberOfExamplesToUse As Nullable(Of Integer) = Nothing, Optional permutationCount As Integer = 1) As ImmutableArray(Of BinaryClassificationMetricsStatistics)類型參數
- TModel
參數
- catalog
- BinaryClassificationCatalog
二元分類目錄。
- predictionTransformer
- ISingleFeaturePredictionTransformer<TModel>
要評估特徵重要性的模型。
- data
- IDataView
評估資料集。
- useFeatureWeightFilter
- Boolean
使用功能權數預先篩選功能。
- permutationCount
- Int32
要執行的排列數目。
傳回
分數的個別功能「貢獻」陣列。
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.BinaryClassification
{
public static class PermutationFeatureImportance
{
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.
var mlContext = new MLContext(seed: 1);
// Create sample data.
var samples = GenerateData();
// Load the sample data as an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Define a training pipeline that concatenates features into a vector,
// normalizes them, and then trains a linear model.
var featureColumns =
new string[] { nameof(Data.Feature1), nameof(Data.Feature2) };
var pipeline = mlContext.Transforms
.Concatenate("Features", featureColumns)
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.BinaryClassification.Trainers
.SdcaLogisticRegression());
// Fit the pipeline to the data.
var model = pipeline.Fit(data);
// Transform the dataset.
var transformedData = model.Transform(data);
// Extract the predictor.
var linearPredictor = model.LastTransformer;
// Compute the permutation metrics for the linear model using the
// normalized data.
var permutationMetrics = mlContext.BinaryClassification
.PermutationFeatureImportance(linearPredictor, transformedData,
permutationCount: 30);
// Now let's look at which features are most important to the model
// overall. Get the feature indices sorted by their impact on AUC.
var sortedIndices = permutationMetrics
.Select((metrics, index) => new { index, metrics.AreaUnderRocCurve })
.OrderByDescending(
feature => Math.Abs(feature.AreaUnderRocCurve.Mean))
.Select(feature => feature.index);
Console.WriteLine("Feature\tModel Weight\tChange in AUC"
+ "\t95% Confidence in the Mean Change in AUC");
var auc = permutationMetrics.Select(x => x.AreaUnderRocCurve).ToArray();
foreach (int i in sortedIndices)
{
Console.WriteLine("{0}\t{1:0.00}\t{2:G4}\t{3:G4}",
featureColumns[i],
linearPredictor.Model.SubModel.Weights[i],
auc[i].Mean,
1.96 * auc[i].StandardError);
}
// Expected output:
// Feature Model Weight Change in AUC 95% Confidence in the Mean Change in AUC
// Feature2 35.15 -0.387 0.002015
// Feature1 17.94 -0.1514 0.0008963
}
private class Data
{
public bool Label { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
/// <summary>
/// Generate an enumerable of Data objects, creating the label as a simple
/// linear combination of the features.
/// </summary>
/// <param name="nExamples">The number of examples.</param>
/// <param name="bias">The bias, or offset, in the calculation of the label.
/// </param>
/// <param name="weight1">The weight to multiply the first feature with to
/// compute the label.</param>
/// <param name="weight2">The weight to multiply the second feature with to
/// compute the label.</param>
/// <param name="seed">The seed for generating feature values and label
/// noise.</param>
/// <returns>An enumerable of Data objects.</returns>
private static IEnumerable<Data> GenerateData(int nExamples = 10000,
double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1)
{
var rng = new Random(seed);
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
var value = (float)(bias + weight1 * data.Feature1 + weight2 *
data.Feature2 + rng.NextDouble() - 0.5);
data.Label = Sigmoid(value) > 0.5;
yield return data;
}
}
private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
}
}
備註
PFI) (排列特徵重要性是判斷定型機器學習模型中特徵的全域重要性的技術。 PFI 是 Breiman 在隨機樹系檔中，第 10 節 (Breiman 所動機的簡單但功能強大的技術。 「隨機樹系」。 Machine Learning，2001.) PFI 方法的優點是其與模型無關，它可與任何可評估的模型搭配使用，而且可以使用任何資料集，而不只是定型集來計算特徵重要性計量。
PFI 的運作方式是採用標示的資料集、選擇特徵，並將該功能的值分散到所有範例中，讓每個範例現在都有特徵的隨機值，以及所有其他特徵的原始值。 評估計量 (例如 AUC) ，然後計算此修改資料集的評估計量，並計算原始資料集中的評估計量變更。 評估計量中的變更愈大，功能對模型而言就越重要。 PFI 的運作方式是跨模型的所有功能，逐一執行此排列分析。
在此實作中，PFI 會計算每個功能的所有可能二進位分類評估計量變更，並 ImmutableArray 傳回 物件的 BinaryClassificationMetrics 。 如需使用這些結果來分析模型特徵重要性的範例，請參閱下列範例。
適用於
PermutationFeatureImportance<TModel>(MulticlassClassificationCatalog, ISingleFeaturePredictionTransformer<TModel>, IDataView, String, Boolean, Nullable<Int32>, Int32)
多類別分類的 PFI) (排列特徵重要性。
public static System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.MulticlassClassificationMetricsStatistics> PermutationFeatureImportance<TModel> (this Microsoft.ML.MulticlassClassificationCatalog catalog, Microsoft.ML.ISingleFeaturePredictionTransformer<TModel> predictionTransformer, Microsoft.ML.IDataView data, string labelColumnName = "Label", bool useFeatureWeightFilter = false, int? numberOfExamplesToUse = default, int permutationCount = 1) where TModel : class;static member PermutationFeatureImportance : Microsoft.ML.MulticlassClassificationCatalog * Microsoft.ML.ISingleFeaturePredictionTransformer<'Model (requires 'Model : null)> * Microsoft.ML.IDataView * string * bool * Nullable<int> * int -> System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.MulticlassClassificationMetricsStatistics> (requires 'Model : null)<Extension()>
Public Function PermutationFeatureImportance(Of TModel As Class) (catalog As MulticlassClassificationCatalog, predictionTransformer As ISingleFeaturePredictionTransformer(Of TModel), data As IDataView, Optional labelColumnName As String = "Label", Optional useFeatureWeightFilter As Boolean = false, Optional numberOfExamplesToUse As Nullable(Of Integer) = Nothing, Optional permutationCount As Integer = 1) As ImmutableArray(Of MulticlassClassificationMetricsStatistics)類型參數
- TModel
參數
- catalog
- MulticlassClassificationCatalog
多類別分類目錄。
- predictionTransformer
- ISingleFeaturePredictionTransformer<TModel>
要評估特徵重要性的模型。
- data
- IDataView
評估資料集。
- labelColumnName
- String
標籤資料行名稱。 資料行資料必須是 KeyDataViewType 。
- useFeatureWeightFilter
- Boolean
使用功能權數預先篩選功能。
- permutationCount
- Int32
要執行的排列數目。
傳回
分數的個別功能「貢獻」陣列。
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.MulticlassClassification
{
public static class PermutationFeatureImportance
{
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.
var mlContext = new MLContext(seed: 1);
// Create sample data.
var samples = GenerateData();
// Load the sample data as an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Define a training pipeline that concatenates features into a vector,
// normalizes them, and then trains a linear model.
var featureColumns =
new string[] { nameof(Data.Feature1), nameof(Data.Feature2) };
var pipeline = mlContext.Transforms
.Concatenate("Features", featureColumns)
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.MulticlassClassification.Trainers
.SdcaMaximumEntropy());
// Fit the pipeline to the data.
var model = pipeline.Fit(data);
// Transform the dataset.
var transformedData = model.Transform(data);
// Extract the predictor.
var linearPredictor = model.LastTransformer;
// Compute the permutation metrics for the linear model using the
// normalized data.
var permutationMetrics = mlContext.MulticlassClassification
.PermutationFeatureImportance(linearPredictor, transformedData,
permutationCount: 30);
// Now let's look at which features are most important to the model
// overall. Get the feature indices sorted by their impact on
// microaccuracy.
var sortedIndices = permutationMetrics
.Select((metrics, index) => new { index, metrics.MicroAccuracy })
.OrderByDescending(feature => Math.Abs(feature.MicroAccuracy.Mean))
.Select(feature => feature.index);
Console.WriteLine("Feature\tChange in MicroAccuracy\t95% Confidence in "
+ "the Mean Change in MicroAccuracy");
var microAccuracy = permutationMetrics.Select(x => x.MicroAccuracy)
.ToArray();
foreach (int i in sortedIndices)
{
Console.WriteLine("{0}\t{1:G4}\t{2:G4}",
featureColumns[i],
microAccuracy[i].Mean,
1.96 * microAccuracy[i].StandardError);
}
// Expected output:
//Feature Change in MicroAccuracy 95% Confidence in the Mean Change in MicroAccuracy
//Feature2 -0.1395 0.0006567
//Feature1 -0.05367 0.0006908
}
private class Data
{
public float Label { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
/// <summary>
/// Generate an enumerable of Data objects, creating the label as a simple
/// linear combination of the features.
/// </summary>
/// <param name="nExamples">The number of examples.</param>
/// <param name="bias">The bias, or offset, in the calculation of the
/// label.</param>
/// <param name="weight1">The weight to multiply the first feature with to
/// compute the label.</param>
/// <param name="weight2">The weight to multiply the second feature with to
/// compute the label.</param>
/// <param name="seed">The seed for generating feature values and label
/// noise.</param>
/// <returns>An enumerable of Data objects.</returns>
private static IEnumerable<Data> GenerateData(int nExamples = 10000,
double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1)
{
var rng = new Random(seed);
var max = bias + 4.5 * weight1 + 4.5 * weight2 + 0.5;
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
var value = (float)
(bias + weight1 * data.Feature1 + weight2 * data.Feature2 +
rng.NextDouble() - 0.5);
if (value < max / 3)
data.Label = 0;
else if (value < 2 * max / 3)
data.Label = 1;
else
data.Label = 2;
yield return data;
}
}
}
}
備註
PFI) (排列特徵重要性是一種技術，可用來判斷定型機器學習模型中特徵的全域重要性。 PFI 是 Breiman 在隨機樹系檔 10 第 10 節 (Breiman 所動機的簡單但功能強大的技術。 「隨機樹系」。 Machine Learning， 2001.) PFI 方法的優點是它與模型無關，它適用于任何可評估的模型，而且可以使用任何資料集，而不只是定型集，來計算特徵重要性計量。
PFI 的運作方式是採用已加上標籤的資料集、選擇特徵，並將該功能的值分散到所有範例中，讓每個範例現在都有特徵的隨機值，以及所有其他特徵的原始值。 評估計量 (例如微精確度) 接著會針對這個修改的資料集計算，並計算原始資料集評估計量的變更。 評估計量的變更愈大，功能對模型而言愈重要。 PFI 的運作方式是跨模型的所有功能執行此排列分析，逐一執行。
在此實作中，PFI 會計算每個功能的所有可能多類別分類評估計量的變更，並 ImmutableArray 傳回 物件的 MulticlassClassificationMetrics 。 如需使用這些結果來分析模型特徵重要性的範例，請參閱下列範例。
適用於
PermutationFeatureImportance<TModel>(RegressionCatalog, ISingleFeaturePredictionTransformer<TModel>, IDataView, String, Boolean, Nullable<Int32>, Int32)
回歸的 PFI) (排列特徵重要性。
public static System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.RegressionMetricsStatistics> PermutationFeatureImportance<TModel> (this Microsoft.ML.RegressionCatalog catalog, Microsoft.ML.ISingleFeaturePredictionTransformer<TModel> predictionTransformer, Microsoft.ML.IDataView data, string labelColumnName = "Label", bool useFeatureWeightFilter = false, int? numberOfExamplesToUse = default, int permutationCount = 1) where TModel : class;static member PermutationFeatureImportance : Microsoft.ML.RegressionCatalog * Microsoft.ML.ISingleFeaturePredictionTransformer<'Model (requires 'Model : null)> * Microsoft.ML.IDataView * string * bool * Nullable<int> * int -> System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.RegressionMetricsStatistics> (requires 'Model : null)<Extension()>
Public Function PermutationFeatureImportance(Of TModel As Class) (catalog As RegressionCatalog, predictionTransformer As ISingleFeaturePredictionTransformer(Of TModel), data As IDataView, Optional labelColumnName As String = "Label", Optional useFeatureWeightFilter As Boolean = false, Optional numberOfExamplesToUse As Nullable(Of Integer) = Nothing, Optional permutationCount As Integer = 1) As ImmutableArray(Of RegressionMetricsStatistics)類型參數
- TModel
參數
- catalog
- RegressionCatalog
回歸目錄。
- predictionTransformer
- ISingleFeaturePredictionTransformer<TModel>
要評估特徵重要性的模型。
- data
- IDataView
評估資料集。
- useFeatureWeightFilter
- Boolean
使用功能權數預先篩選功能。
- permutationCount
- Int32
要執行的排列數目。
傳回
分數的個別功能「貢獻」陣列。
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.Regression
{
public static class PermutationFeatureImportance
{
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.
var mlContext = new MLContext(seed: 1);
// Create sample data.
var samples = GenerateData();
// Load the sample data as an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Define a training pipeline that concatenates features into a vector,
// normalizes them, and then trains a linear model.
var featureColumns = new string[] { nameof(Data.Feature1),
nameof(Data.Feature2) };
var pipeline = mlContext.Transforms.Concatenate(
"Features",
featureColumns)
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.Regression.Trainers.Ols());
// Fit the pipeline to the data.
var model = pipeline.Fit(data);
// Transform the dataset.
var transformedData = model.Transform(data);
// Extract the predictor.
var linearPredictor = model.LastTransformer;
// Compute the permutation metrics for the linear model using the
// normalized data.
var permutationMetrics = mlContext.Regression
.PermutationFeatureImportance(
linearPredictor, transformedData, permutationCount: 30);
// Now let's look at which features are most important to the model
// overall. Get the feature indices sorted by their impact on RMSE.
var sortedIndices = permutationMetrics
.Select((metrics, index) => new
{
index,
metrics.RootMeanSquaredError
})
.OrderByDescending(feature => Math.Abs(
feature.RootMeanSquaredError.Mean))
.Select(feature => feature.index);
Console.WriteLine("Feature\tModel Weight\tChange in RMSE\t95%" +
"Confidence in the Mean Change in RMSE");
var rmse = permutationMetrics.Select(x => x.RootMeanSquaredError)
.ToArray();
foreach (int i in sortedIndices)
{
Console.WriteLine("{0}\t{1:0.00}\t{2:G4}\t{3:G4}",
featureColumns[i],
linearPredictor.Model.Weights[i],
rmse[i].Mean,
1.96 * rmse[i].StandardError);
}
// Expected output:
// Feature Model Weight Change in RMSE 95% Confidence in the Mean Change in RMSE
// Feature2 9.00 4.009 0.008304
// Feature1 4.48 1.901 0.003351
}
private class Data
{
public float Label { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
/// <summary>
/// Generate an enumerable of Data objects, creating the label as a simple
/// linear combination of the features.
/// </summary>
/// <param name="nExamples">The number of examples.</param>
/// <param name="bias">The bias, or offset, in the calculation of the label.
/// </param>
/// <param name="weight1">The weight to multiply the first feature with to
/// compute the label.</param>
/// <param name="weight2">The weight to multiply the second feature with to
/// compute the label.</param>
/// <param name="seed">The seed for generating feature values and label
/// noise.</param>
/// <returns>An enumerable of Data objects.</returns>
private static IEnumerable<Data> GenerateData(int nExamples = 10000,
double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1)
{
var rng = new Random(seed);
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
data.Label = (float)(bias + weight1 * data.Feature1 + weight2 *
data.Feature2 + rng.NextDouble() - 0.5);
yield return data;
}
}
}
}
備註
PFI) (排列特徵重要性是一種技術，可用來判斷定型機器學習模型中特徵的全域重要性。 PFI 是 Breiman 在隨機樹系檔 10 第 10 節 (Breiman 所動機的簡單但功能強大的技術。 「隨機樹系」。 Machine Learning， 2001.) PFI 方法的優點是它與模型無關，它適用于任何可評估的模型，而且可以使用任何資料集，而不只是定型集，來計算特徵重要性計量。
PFI 的運作方式是採用已加上標籤的資料集、選擇特徵，並將該功能的值分散到所有範例中，讓每個範例現在都有特徵的隨機值，以及所有其他特徵的原始值。 評估計量 (例如 R 平方) ，然後計算此修改資料集的評估計量變更，並計算原始資料集中的評估計量變更。 評估計量的變更愈大，功能對模型而言愈重要。 PFI 的運作方式是跨模型的所有功能執行此排列分析，逐一執行。
在此實作中，PFI 會計算每個特徵之所有可能回歸評估計量的變更，並 ImmutableArray 傳回 物件的 RegressionMetrics 。 如需使用這些結果來分析模型特徵重要性的範例，請參閱下列範例。
適用於
PermutationFeatureImportance<TModel>(RankingCatalog, ISingleFeaturePredictionTransformer<TModel>, IDataView, String, String, Boolean, Nullable<Int32>, Int32)
排名 (PFI) 排列特徵重要性。
public static System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.RankingMetricsStatistics> PermutationFeatureImportance<TModel> (this Microsoft.ML.RankingCatalog catalog, Microsoft.ML.ISingleFeaturePredictionTransformer<TModel> predictionTransformer, Microsoft.ML.IDataView data, string labelColumnName = "Label", string rowGroupColumnName = "GroupId", bool useFeatureWeightFilter = false, int? numberOfExamplesToUse = default, int permutationCount = 1) where TModel : class;static member PermutationFeatureImportance : Microsoft.ML.RankingCatalog * Microsoft.ML.ISingleFeaturePredictionTransformer<'Model (requires 'Model : null)> * Microsoft.ML.IDataView * string * string * bool * Nullable<int> * int -> System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.RankingMetricsStatistics> (requires 'Model : null)<Extension()>
Public Function PermutationFeatureImportance(Of TModel As Class) (catalog As RankingCatalog, predictionTransformer As ISingleFeaturePredictionTransformer(Of TModel), data As IDataView, Optional labelColumnName As String = "Label", Optional rowGroupColumnName As String = "GroupId", Optional useFeatureWeightFilter As Boolean = false, Optional numberOfExamplesToUse As Nullable(Of Integer) = Nothing, Optional permutationCount As Integer = 1) As ImmutableArray(Of RankingMetricsStatistics)類型參數
- TModel
參數
- catalog
- RankingCatalog
排名目錄。
- predictionTransformer
- ISingleFeaturePredictionTransformer<TModel>
要評估特徵重要性的模型。
- data
- IDataView
評估資料集。
- labelColumnName
- String
標籤資料行名稱。 資料行資料必須是 Single 或 KeyDataViewType 。
- rowGroupColumnName
- String
GroupId 資料行名稱
- useFeatureWeightFilter
- Boolean
使用功能權數預先篩選功能。
- permutationCount
- Int32
要執行的排列數目。
傳回
分數的個別功能「貢獻」陣列。
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.Ranking
{
public static class PermutationFeatureImportance
{
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.
var mlContext = new MLContext(seed: 1);
// Create sample data.
var samples = GenerateData();
// Load the sample data as an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Define a training pipeline that concatenates features into a vector,
// normalizes them, and then trains a linear model.
var featureColumns = new string[] { nameof(Data.Feature1), nameof(
Data.Feature2) };
var pipeline = mlContext.Transforms.Concatenate("Features",
featureColumns)
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
.Append(mlContext.Transforms.Conversion.MapValueToKey(
"GroupId"))
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.Ranking.Trainers.FastTree());
// Fit the pipeline to the data.
var model = pipeline.Fit(data);
// Transform the dataset.
var transformedData = model.Transform(data);
// Extract the predictor.
var linearPredictor = model.LastTransformer;
// Compute the permutation metrics for the linear model using the
// normalized data.
var permutationMetrics = mlContext.Ranking.PermutationFeatureImportance(
linearPredictor, transformedData, permutationCount: 30);
// Now let's look at which features are most important to the model
// overall. Get the feature indices sorted by their impact on NDCG@1.
var sortedIndices = permutationMetrics.Select((metrics, index) => new
{
index,
metrics.NormalizedDiscountedCumulativeGains
})
.OrderByDescending(feature => Math.Abs(
feature.NormalizedDiscountedCumulativeGains[0].Mean))
.Select(feature => feature.index);
Console.WriteLine("Feature\tChange in NDCG@1\t95% Confidence in the" +
"Mean Change in NDCG@1");
var ndcg = permutationMetrics.Select(
x => x.NormalizedDiscountedCumulativeGains).ToArray();
foreach (int i in sortedIndices)
{
Console.WriteLine("{0}\t{1:G4}\t{2:G4}",
featureColumns[i],
ndcg[i][0].Mean,
1.96 * ndcg[i][0].StandardError);
}
// Expected output:
// Feature Change in NDCG@1 95% Confidence in the Mean Change in NDCG@1
// Feature2 -0.2421 0.001748
// Feature1 -0.0513 0.001184
}
private class Data
{
public float Label { get; set; }
public int GroupId { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
/// <summary>
/// Generate an enumerable of Data objects, creating the label as a simple
/// linear combination of the features.
/// </summary>
///
/// <param name="nExamples">The number of examples.</param>
///
/// <param name="bias">The bias, or offset, in the calculation of the label.
/// </param>
///
/// <param name="weight1">The weight to multiply the first feature with to
/// compute the label.</param>
///
/// <param name="weight2">The weight to multiply the second feature with to
/// compute the label.</param>
///
/// <param name="seed">The seed for generating feature values and label
/// noise.</param>
///
/// <returns>An enumerable of Data objects.</returns>
private static IEnumerable<Data> GenerateData(int nExamples = 10000,
double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1,
int groupSize = 5)
{
var rng = new Random(seed);
var max = bias + 4.5 * weight1 + 4.5 * weight2 + 0.5;
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
GroupId = i / groupSize,
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
var value = (float)(bias + weight1 * data.Feature1 + weight2 *
data.Feature2 + rng.NextDouble() - 0.5);
if (value < max / 3)
data.Label = 0;
else if (value < 2 * max / 3)
data.Label = 1;
else
data.Label = 2;
yield return data;
}
}
}
}
備註
PFI) (排列特徵重要性是一種技術，可用來判斷定型機器學習模型中特徵的全域重要性。 PFI 是 Breiman 在隨機樹系檔 10 第 10 節 (Breiman 所動機的簡單但功能強大的技術。 「隨機樹系」。 Machine Learning， 2001.) PFI 方法的優點是它與模型無關，它適用于任何可評估的模型，而且可以使用任何資料集，而不只是定型集，來計算特徵重要性計量。
PFI 的運作方式是採用已加上標籤的資料集、選擇特徵，並將該功能的值分散到所有範例中，讓每個範例現在都有特徵的隨機值，以及所有其他特徵的原始值。 評估計量 (例如 NDCG) ，然後計算此修改資料集的評估計量變更，並計算原始資料集中的評估計量變更。 評估計量的變更愈大，功能對模型而言愈重要。 PFI 的運作方式是跨模型的所有功能執行此排列分析，逐一執行。
在此實作中，PFI 會計算每個功能之所有可能排名評估計量的變更，並 ImmutableArray 傳回 物件的 RankingMetrics 。 如需使用這些結果來分析模型特徵重要性的範例，請參閱下列範例。
適用於
意見反應
即將登場：在 2024 年，我們將逐步淘汰 GitHub 問題作為內容的意見反應機制，並將它取代為新的意見反應系統。 如需詳細資訊，請參閱：https://aka.ms/ContentUserFeedback。
提交並檢視相關的意見反應