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‎docs/api-reference/algo-details-lightgbm.md

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### Training Algorithm Details
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LightGBM is an open source implementation of gradient boosting decision tree.
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For implementation details, please see [LightGBM's official
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documentation](https://lightgbm.readthedocs.io/en/latest/index.html) or this
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[paper](https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf).

‎src/Microsoft.ML.FastTree/FastTreeClassification.cs

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/// ]]>
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/// </format>
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/// </remarks>
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/// <seealso cref="Microsoft.ML.TreeExtensions.FastTree(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,System.Int32,System.Int32,System.Int32,System.Double)"/>
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/// <seealso cref="Microsoft.ML.TreeExtensions.FastTree(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.FastTree.FastTreeBinaryTrainer.Options)"/>
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/// <seealso cref="TreeExtensions.FastTree(BinaryClassificationCatalog.BinaryClassificationTrainers, string, string, string, int, int, int, double)"/>
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/// <seealso cref="TreeExtensions.FastTree(BinaryClassificationCatalog.BinaryClassificationTrainers, FastTreeBinaryTrainer.Options)"/>
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/// <seealso cref="Options"/>
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public sealed partial class FastTreeBinaryTrainer :
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BoostingFastTreeTrainerBase<FastTreeBinaryTrainer.Options,

‎src/Microsoft.ML.FastTree/FastTreeRegression.cs

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/// ]]>
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/// </format>
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/// </remarks>
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/// <seealso cref="Microsoft.ML.TreeExtensions.FastTree(Microsoft.ML.RegressionCatalog.RegressionTrainers,System.String,System.String,System.String,System.Int32,System.Int32,System.Int32,System.Double)"/>
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/// <seealso cref="Microsoft.ML.TreeExtensions.FastTree(Microsoft.ML.RegressionCatalog.RegressionTrainers,Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer.Options)"/>
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/// <seealso cref="TreeExtensions.FastTree(RegressionCatalog.RegressionTrainers, string, string, string, int, int, int, double)"/>
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/// <seealso cref="TreeExtensions.FastTree(RegressionCatalog.RegressionTrainers, FastTreeRegressionTrainer.Options)"/>
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/// <seealso cref="Options"/>
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public sealed partial class FastTreeRegressionTrainer
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: BoostingFastTreeTrainerBase<FastTreeRegressionTrainer.Options, RegressionPredictionTransformer<FastTreeRegressionModelParameters>, FastTreeRegressionModelParameters>

‎src/Microsoft.ML.FastTree/RandomForestClassification.cs

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/// ]]>
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/// </format>
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/// </remarks>
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/// <seealso cref="Microsoft.ML.TreeExtensions.FastForest(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,System.Int32,System.Int32,System.Int32)"/>
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/// <seealso cref="Microsoft.ML.TreeExtensions.FastForest(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.FastTree.FastForestBinaryTrainer.Options)"/>
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/// <seealso cref="TreeExtensions.FastForest(BinaryClassificationCatalog.BinaryClassificationTrainers, string, string, string, int, int, int)"/>
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/// <seealso cref="TreeExtensions.FastForest(BinaryClassificationCatalog.BinaryClassificationTrainers, FastForestBinaryTrainer.Options)"/>
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/// <seealso cref="Options"/>
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public sealed partial class FastForestBinaryTrainer :
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RandomForestTrainerBase<FastForestBinaryTrainer.Options, BinaryPredictionTransformer<FastForestBinaryModelParameters>, FastForestBinaryModelParameters>

‎src/Microsoft.ML.FastTree/RandomForestRegression.cs

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/// ]]>
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/// </format>
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/// </remarks>
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/// <seealso cref="Microsoft.ML.TreeExtensions.FastForest(Microsoft.ML.RegressionCatalog.RegressionTrainers,System.String,System.String,System.String,System.Int32,System.Int32,System.Int32)"/>
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/// <seealso cref="Microsoft.ML.TreeExtensions.FastForest(Microsoft.ML.RegressionCatalog.RegressionTrainers,Microsoft.ML.Trainers.FastTree.FastForestRegressionTrainer.Options)"/>
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/// <seealso cref="TreeExtensions.FastForest(RegressionCatalog.RegressionTrainers, string, string, string, int, int, int)"/>
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/// <seealso cref="TreeExtensions.FastForest(RegressionCatalog.RegressionTrainers, FastForestRegressionTrainer.Options)"/>
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/// <seealso cref="Options"/>
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public sealed partial class FastForestRegressionTrainer
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: RandomForestTrainerBase<FastForestRegressionTrainer.Options, RegressionPredictionTransformer<FastForestRegressionModelParameters>, FastForestRegressionModelParameters>

‎src/Microsoft.ML.LightGbm/LightGbmCatalog.cs

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}
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/// <summary>
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/// Creates <see cref="LightGbmRankingTrainer"/>, which predict a target using a gradient boosting decision tree ranking model.
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/// Create <see cref="LightGbmRankingTrainer"/>, which predict a target using a gradient boosting decision tree ranking model.
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/// </summary>
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/// <param name="catalog">The <see cref="RankingCatalog"/>.</param>
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/// <param name="labelColumnName">The name of the label column. The column data must be <see cref="System.Single"/>.</param>
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}
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/// <summary>
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/// Creates <see cref="LightGbmRankingTrainer"/>, which predict a target using a gradient boosting decision tree ranking model with advanced options.
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/// Create <see cref="LightGbmRankingTrainer"/> with advanced options, which predict a target using a gradient boosting decision tree ranking model.
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/// </summary>
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/// <param name="catalog">The <see cref="RankingCatalog"/>.</param>
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/// <param name="options">Trainer options.</param>

‎src/Microsoft.ML.LightGbm/LightGbmRankingTrainer.cs

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/// | | |
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/// | -- | -- |
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/// | Machine learning task | Ranking |
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/// | Is normalization required? | Yes |
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/// | Is normalization required? | No |
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/// | Is caching required? | No |
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/// | Required NuGet in addition to Microsoft.ML | Microsoft.ML.FastTree |
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///
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/// ### Training Algorithm Details
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/// LightGBM is an open source implementation of gradient boosting decision tree. For implementation details, please see LightGBM's official [documentation](https://lightgbm.readthedocs.io/en/latest/index.html) or
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/// [this](https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf) paper.
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/// [!include[algorithm](~/../docs/samples/docs/api-reference/algo-details-lightgbm.md)]
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/// ]]>
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/// </format>
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/// </remarks>
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/// <seealso cref="Microsoft.ML.LightGbmExtensions.LightGbm(Microsoft.ML.RankingCatalog.RankingTrainers,System.String,System.String,System.String,System.String,System.Nullable{System.Int32},System.Nullable{System.Int32},System.Nullable{System.Double},System.Int32)"/>
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/// <seealso cref="Microsoft.ML.LightGbmExtensions.LightGbm(Microsoft.ML.RankingCatalog.RankingTrainers,Microsoft.ML.Trainers.LightGbm.LightGbmRankingTrainer.Options)"/>
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/// <seealso cref="LightGbmExtensions.LightGbm(RankingCatalog.RankingTrainers, string, string, string, string, int?, int?, double?, int)"/>
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/// <seealso cref="LightGbmExtensions.LightGbm(RankingCatalog.RankingTrainers, LightGbmRankingTrainer.Options)"/>
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/// <seealso cref="Options"/>
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public sealed class LightGbmRankingTrainer : LightGbmTrainerBase<LightGbmRankingTrainer.Options,
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float,
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/// </summary>
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[Argument(ArgumentType.AtMostOnce, HelpText = "An array of gains associated to each relevance label.", ShortName = "gains")]
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[TGUI(Label = "Ranking Label Gain")]
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public int[] CustomGains = { 0,3,7,15,31,63,127,255,511,1023,2047,4095 };
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public int[] CustomGains = { 0, 3, 7, 15, 31, 63, 127, 255, 511, 1023, 2047, 4095 };
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/// <summary>
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/// Parameter for the sigmoid function.
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{
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var res = base.ToDictionary(host);
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res[GetOptionName(nameof(Sigmoid))] = Sigmoid;
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res[GetOptionName(nameof(CustomGains))] = string.Join(",",CustomGains);
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if(EvaluationMetric != EvaluateMetricType.Default)
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res[GetOptionName(nameof(CustomGains))] = string.Join(",", CustomGains);
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if (EvaluationMetric != EvaluateMetricType.Default)
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res[GetOptionName(nameof(EvaluateMetricType))] = GetOptionName(EvaluationMetric.ToString());
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return res;
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: this(env,
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new Options()
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{
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LabelColumnName = labelColumnName,
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FeatureColumnName = featureColumnName,
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ExampleWeightColumnName = weightsColumnName,
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RowGroupColumnName = rowGroupdColumnName,
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NumberOfLeaves = numberOfLeaves,
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MinimumExampleCountPerLeaf = minimumExampleCountPerLeaf,
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LearningRate = learningRate,
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NumberOfIterations = numberOfIterations
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LabelColumnName = labelColumnName,
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FeatureColumnName = featureColumnName,
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ExampleWeightColumnName = weightsColumnName,
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RowGroupColumnName = rowGroupdColumnName,
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NumberOfLeaves = numberOfLeaves,
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MinimumExampleCountPerLeaf = minimumExampleCountPerLeaf,
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LearningRate = learningRate,
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NumberOfIterations = numberOfIterations
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})
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{
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Host.CheckNonEmpty(rowGroupdColumnName, nameof(rowGroupdColumnName));

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