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Merge pull request #2067 from shauheen/release/v09rc2
Update release for 0.9
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‎Microsoft.ML.sln‎

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@@ -23,8 +23,6 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.StandardLearne
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Transforms", "src\Microsoft.ML.Transforms\Microsoft.ML.Transforms.csproj", "{2911A286-ECA4-4730-97A9-DA1FEE2DED97}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Legacy", "src\Microsoft.ML.Legacy\Microsoft.ML.Legacy.csproj", "{7288C084-11C0-43BE-AC7F-45DCFEAEEBF6}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.KMeansClustering", "src\Microsoft.ML.KMeansClustering\Microsoft.ML.KMeansClustering.csproj", "{F1CAE3AB-4F86-4BC0-BBA8-C4A58E7E8A4A}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.PCA", "src\Microsoft.ML.PCA\Microsoft.ML.PCA.csproj", "{58E06735-1129-4DD5-86E0-6BBFF049AAD9}"
@@ -147,7 +145,7 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.HalLearners.St
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.OnnxTransform.StaticPipe", "src\Microsoft.ML.OnnxTransform.StaticPipe\Microsoft.ML.OnnxTransform.StaticPipe.csproj", "{D1324668-9568-40F4-AA55-30A9A516C230}"
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EndProject
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Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Microsoft.ML.LightGBM.StaticPipe", "src\Microsoft.ML.LightGBM.StaticPipe\Microsoft.ML.LightGBM.StaticPipe.csproj", "{22C51B08-ACAE-47B2-A312-462DC239A23B}"
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.LightGBM.StaticPipe", "src\Microsoft.ML.LightGBM.StaticPipe\Microsoft.ML.LightGBM.StaticPipe.csproj", "{22C51B08-ACAE-47B2-A312-462DC239A23B}"
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EndProject
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Global
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GlobalSection(SolutionConfigurationPlatforms) = preSolution
@@ -213,14 +211,6 @@ Global
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{2911A286-ECA4-4730-97A9-DA1FEE2DED97}.Release|Any CPU.Build.0 = Release|Any CPU
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{2911A286-ECA4-4730-97A9-DA1FEE2DED97}.Release-Intrinsics|Any CPU.ActiveCfg = Release-Intrinsics|Any CPU
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{2911A286-ECA4-4730-97A9-DA1FEE2DED97}.Release-Intrinsics|Any CPU.Build.0 = Release-Intrinsics|Any CPU
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{7288C084-11C0-43BE-AC7F-45DCFEAEEBF6}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{7288C084-11C0-43BE-AC7F-45DCFEAEEBF6}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{7288C084-11C0-43BE-AC7F-45DCFEAEEBF6}.Debug-Intrinsics|Any CPU.ActiveCfg = Debug-Intrinsics|Any CPU
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{7288C084-11C0-43BE-AC7F-45DCFEAEEBF6}.Debug-Intrinsics|Any CPU.Build.0 = Debug-Intrinsics|Any CPU
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{7288C084-11C0-43BE-AC7F-45DCFEAEEBF6}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{7288C084-11C0-43BE-AC7F-45DCFEAEEBF6}.Release|Any CPU.Build.0 = Release|Any CPU
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{7288C084-11C0-43BE-AC7F-45DCFEAEEBF6}.Release-Intrinsics|Any CPU.ActiveCfg = Release-Intrinsics|Any CPU
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{7288C084-11C0-43BE-AC7F-45DCFEAEEBF6}.Release-Intrinsics|Any CPU.Build.0 = Release-Intrinsics|Any CPU
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{F1CAE3AB-4F86-4BC0-BBA8-C4A58E7E8A4A}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{F1CAE3AB-4F86-4BC0-BBA8-C4A58E7E8A4A}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{F1CAE3AB-4F86-4BC0-BBA8-C4A58E7E8A4A}.Debug-Intrinsics|Any CPU.ActiveCfg = Debug-Intrinsics|Any CPU
@@ -593,7 +583,6 @@ Global
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{65D0603E-B96C-4DFC-BDD1-705891B88C18} = {09EADF06-BE25-4228-AB53-95AE3E15B530}
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{707BB22C-7E5F-497A-8C2F-74578F675705} = {09EADF06-BE25-4228-AB53-95AE3E15B530}
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{2911A286-ECA4-4730-97A9-DA1FEE2DED97} = {09EADF06-BE25-4228-AB53-95AE3E15B530}
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{7288C084-11C0-43BE-AC7F-45DCFEAEEBF6} = {09EADF06-BE25-4228-AB53-95AE3E15B530}
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{F1CAE3AB-4F86-4BC0-BBA8-C4A58E7E8A4A} = {09EADF06-BE25-4228-AB53-95AE3E15B530}
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{58E06735-1129-4DD5-86E0-6BBFF049AAD9} = {09EADF06-BE25-4228-AB53-95AE3E15B530}
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{64BC22D3-1E76-41EF-94D8-C79E471FF2DD} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4}

‎build/Dependencies.props‎

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<PropertyGroup>
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<GoogleProtobufPackageVersion>3.5.1</GoogleProtobufPackageVersion>
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<LightGBMPackageVersion>2.2.1.1</LightGBMPackageVersion>
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<MicrosoftMLOnnxRuntimePackageVersion>0.1.5</MicrosoftMLOnnxRuntimePackageVersion>
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<MicrosoftMLOnnxRuntimeGpuPackageVersion>0.1.5</MicrosoftMLOnnxRuntimeGpuPackageVersion>
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<MlNetMklDepsPackageVersion>0.0.0.7</MlNetMklDepsPackageVersion>
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<ParquetDotNetPackageVersion>2.1.3</ParquetDotNetPackageVersion>
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<SystemDrawingCommonPackageVersion>4.5.0</SystemDrawingCommonPackageVersion>

‎docs/release-notes/0.9/release-0.9.md‎

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# ML.NET 0.9 Release Notes
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Welcome to 2019! For the past 9 months we have been adding features and improving [ML.NET](https://aka.ms/mlnet). In the forthcoming 0.10, 0.11, and 0.12 releases before we reach 1.0, we will focus on the overall stability of the package, continue to refine the API, increase test coverage and improve documentation. 0.9 release packs multiple fixes as well as significant clean up to the internal code of the package.
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### Installation
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ML.NET supports Windows, MacOS, and Linux. See [supported OS versions of .NET
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Core
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2.0](https://github.com/dotnet/core/blob/master/release-notes/2.0/2.0-supported-os.md)
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for more details.
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You can install ML.NET NuGet from the CLI using:
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```
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dotnet add package Microsoft.ML
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```
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From package manager:
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```
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Install-Package Microsoft.ML
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```
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### Release Notes
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Below are a few of the highlights from this release. There are many other improvements in the API.
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* Added Feature Contribution Calculation
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([#1847](https://github.com/dotnet/machinelearning/pull/1847))
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* FCC can be used to compute feature contributions in addition to the overall prediction when models are evaluated.
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* Removed Legacy namespace that was marked obsolete
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([#2043](https://github.com/dotnet/machinelearning/pull/2043))
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* GPU support for ONNX Transform
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([#1922](https://github.com/dotnet/machinelearning/pull/1922))
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* GPU is currently supported on 64 bit Windows
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* Cross platform support is still being developed for this feature
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* `Permutation Feature Importance` now supports confidence intervals
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([#1844](https://github.com/dotnet/machinelearning/pull/1844))
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* Introducing `PredictionEngine` instead of `PredictionFunction`
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([#1920](https://github.com/dotnet/machinelearning/pull/1920))
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### Acknowledgements
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Shoutout to [dhilmathy](https://github.com/dhilmathy),
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[mnboos](https://github.com/mnboos),
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[robosek](https://github.com/robosek), and the [ML.NET](https://aka.ms/mlnet) team for their
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contributions as part of this release!

‎docs/samples/Microsoft.ML.Samples/Dynamic/FeatureContributionCalculationTransform.cs‎

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@@ -8,7 +8,7 @@ public class FeatureContributionCalculationTransform_RegressionExample
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public static void FeatureContributionCalculationTransform_Regression()
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{
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// Downloading the dataset from github.com/dotnet/machinelearning.
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// This will create a sentiment.tsv file in the filesystem.
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// This will create a housing.txt file in the filesystem.
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// You can open this file, if you want to see the data.
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string dataFile = SamplesUtils.DatasetUtils.DownloadHousingRegressionDataset();
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// Create a Feature Contribution Calculator
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// Calculate the feature contributions for all features given trained model parameters
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// And don't normalize the contribution scores
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var featureContributionCalculator = mlContext.Model.Explainability.FeatureContributionCalculation(model.Model, model.FeatureColumn, top: 11, normalize: false);
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var featureContributionCalculator = mlContext.Model.Explainability.FeatureContributionCalculation(model.Model, model.FeatureColumn, numPositiveContributions: 11, normalize: false);
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var outputData = featureContributionCalculator.Fit(scoredData).Transform(scoredData);
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// FeatureContributionCalculatingEstimator can be use as an intermediary step in a pipeline.
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// The features retained by FeatureContributionCalculatingEstimator will be in the FeatureContribution column.
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var pipeline = mlContext.Model.Explainability.FeatureContributionCalculation(model.Model, model.FeatureColumn, top: 11)
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var pipeline = mlContext.Model.Explainability.FeatureContributionCalculation(model.Model, model.FeatureColumn, numPositiveContributions: 11)
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.Append(mlContext.Regression.Trainers.OrdinaryLeastSquares(featureColumn: "FeatureContributions"));
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var outData = featureContributionCalculator.Fit(scoredData).Transform(scoredData);
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var weights = new VBuffer<float>();
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model.Model.GetFeatureWeights(ref weights);
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// Let's now walk through the first ten reconds and see which feature drove the values the most
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// Let's now walk through the first ten records and see which feature drove the values the most
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// Get prediction scores and contributions
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var scoringEnumerator = outputData.AsEnumerable<HousingRegressionScoreAndContribution>(mlContext, true).GetEnumerator();
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int index = 0;

‎docs/samples/Microsoft.ML.Samples/Static/AveragedPerceptronBinaryClassification.cs‎

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var mlContext = new MLContext();
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// Creating Data Reader with the initial schema based on the format of the data
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var reader = TextLoader.CreateReader(
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var reader = TextLoaderStatic.CreateReader(
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mlContext,
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c => (
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Age: c.LoadFloat(0),

‎docs/samples/Microsoft.ML.Samples/Static/FastTreeBinaryClassification.cs‎

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var mlContext = new MLContext();
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// Creating Data Reader with the initial schema based on the format of the data
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var reader = TextLoader.CreateReader(
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var reader = TextLoaderStatic.CreateReader(
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mlContext,
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c => (
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Age: c.LoadFloat(0),

‎docs/samples/Microsoft.ML.Samples/Static/FastTreeRegression.cs‎

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var mlContext = new MLContext();
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// Creating a data reader, based on the format of the data
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var reader = TextLoader.CreateReader(mlContext, c => (
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var reader = TextLoaderStatic.CreateReader(mlContext, c => (
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label: c.LoadFloat(0),
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features: c.LoadFloat(1, 6)
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),

‎docs/samples/Microsoft.ML.Samples/Static/FeatureSelectionTransform.cs‎

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// all the feature columns into entries of a vector of a single column named "Features".
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var reader = TextLoader.CreateReader(ml, c => (
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var reader = TextLoaderStatic.CreateReader(ml, c => (
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Label: c.LoadBool(0),
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Features: c.LoadFloat(1, 9)
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),

‎docs/samples/Microsoft.ML.Samples/Static/LightGBMBinaryClassification.cs‎

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var mlContext = new MLContext();
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// Creating Data Reader with the initial schema based on the format of the data
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var reader = TextLoader.CreateReader(
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var reader = TextLoaderStatic.CreateReader(
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‎docs/samples/Microsoft.ML.Samples/Static/LightGBMRegression.cs‎

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using Microsoft.ML.Data;
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using Microsoft.ML.LightGBM;
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using Microsoft.ML.LightGBM.StaticPipe;
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using Microsoft.ML.StaticPipe;
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namespace Microsoft.ML.Samples.Static
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{
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var mlContext = new MLContext();
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// Creating a data reader, based on the format of the data
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var reader = TextLoader.CreateReader(mlContext, c => (
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var reader = TextLoaderStatic.CreateReader(mlContext, c => (
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label: c.LoadFloat(0),
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features: c.LoadFloat(1, 6)
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),

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