/// <summary>
/// Create <see cref= "SymbolicSgdLogisticRegressionBinaryTrainer" /> with advanced options, which predicts a target using a linear binary classification model trained over boolean label data.
/// Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function.
/// The <see cref="SymbolicSgdLogisticRegressionBinaryTrainer"/> parallelizes SGD using <a href="https://www.microsoft.com/en-us/research/project/project-parade/#!symbolic-execution">symbolic execution</a>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="options">Algorithm advanced options. See <see cref="SymbolicSgdLogisticRegressionBinaryTrainer.Options"/>.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static SymbolicSgdLogisticRegressionBinaryTrainer SymbolicSgdLogisticRegression(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
SymbolicSgdLogisticRegressionBinaryTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
Contracts.CheckValue(options, nameof(options));
var env = CatalogUtils.GetEnvironment(catalog);
return(new SymbolicSgdLogisticRegressionBinaryTrainer(env, options));
}
/// <summary>
/// Predict a target using a linear binary classification model trained with the <see cref="SymSgdClassificationTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="options">Algorithm advanced options. See <see cref="SymSgdClassificationTrainer.Options"/>.</param>
public static SymSgdClassificationTrainer SymbolicStochasticGradientDescent(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
SymSgdClassificationTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
Contracts.CheckValue(options, nameof(options));
var env = CatalogUtils.GetEnvironment(catalog);
return(new SymSgdClassificationTrainer(env, options));
}
/// <summary>
/// Predict a target using a decision tree binary classification model trained with the <see cref="FastTreeBinaryClassificationTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="options">Algorithm advanced settings.</param>
public static FastTreeBinaryClassificationTrainer FastTree(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
FastTreeBinaryClassificationTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
Contracts.CheckValue(options, nameof(options));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FastTreeBinaryClassificationTrainer(env, options));
}
/// <summary>
/// Predict a target using a linear binary classification model trained with <see cref="AveragedPerceptronTrainer"/> and advanced options.
/// </summary>
/// <param name="catalog">The binary classification catalog trainer object.</param>
/// <param name="options">Trainer options.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static AveragedPerceptronTrainer AveragedPerceptron(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, AveragedPerceptronTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
Contracts.CheckValue(options, nameof(options));
var env = CatalogUtils.GetEnvironment(catalog);
return(new AveragedPerceptronTrainer(env, options));
}
/// <summary>
/// Predict a target using a linear binary classification model trained with the SDCA trainer.
/// </summary>
/// <param name="catalog">The binary classification catalog trainer object.</param>
/// <param name="options">Advanced arguments to the algorithm.</param>
public static SdcaNonCalibratedBinaryTrainer StochasticDualCoordinateAscentNonCalibrated(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
SdcaNonCalibratedBinaryTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
Contracts.CheckValue(options, nameof(options));
var env = CatalogUtils.GetEnvironment(catalog);
return(new SdcaNonCalibratedBinaryTrainer(env, options));
}
/// <summary>
/// Predict a target using a decision tree regression model trained with the <see cref="FastForestClassification"/>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="labelColumn">The labelColumn column.</param>
/// <param name="featureColumn">The featureColumn column.</param>
/// <param name="weights">The optional weights column.</param>
/// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
/// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
public static FastForestClassification FastForest(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FastForestClassification(env, labelColumn, featureColumn, weights, numLeaves, numTrees, minDatapointsInLeaves));
}
/// <summary>
/// Predict a target using a decision tree binary classification model trained with the <see cref="LightGbmBinaryTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="labelColumn">The labelColumn column.</param>
/// <param name="featureColumn">The features column.</param>
/// <param name="weights">The weights column.</param>
/// <param name="numLeaves">The number of leaves to use.</param>
/// <param name="numBoostRound">Number of iterations.</param>
/// <param name="minDataPerLeaf">The minimal number of documents allowed in a leaf of the tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LightGbmBinaryTrainer LightGbm(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
int? numLeaves = null,
int? minDataPerLeaf = null,
double? learningRate = null,
int numBoostRound = Options.Defaults.NumBoostRound)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return new LightGbmBinaryTrainer(env, labelColumn, featureColumn, weights, numLeaves, minDataPerLeaf, learningRate, numBoostRound);
}
/// <summary>
/// Predict a target using generalized additive models trained with the <see cref="BinaryClassificationGamTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="labelColumn">The labelColumn column.</param>
/// <param name="featureColumn">The featureColumn column.</param>
/// <param name="weights">The optional weights column.</param>
/// <param name="numIterations">The number of iterations to use in learning the features.</param>
/// <param name="learningRate">The learning rate. GAMs work best with a small learning rate.</param>
/// <param name="maxBins">The maximum number of bins to use to approximate features.</param>
public static BinaryClassificationGamTrainer GeneralizedAdditiveModels(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
int numIterations = GamDefaults.NumIterations,
double learningRate = GamDefaults.LearningRates,
int maxBins = GamDefaults.MaxBins)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new BinaryClassificationGamTrainer(env, labelColumn, featureColumn, weights, numIterations, learningRate, maxBins));
}
/// <summary>
/// Predict a target using generalized additive models trained with the <see cref="BinaryClassificationGamTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="featureColumnName">The name of the feature column.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
/// <param name="numberOfIterations">The number of iterations to use in learning the features.</param>
/// <param name="maximumBinCountPerFeature">The maximum number of bins to use to approximate features.</param>
/// <param name="learningRate">The learning rate. GAMs work best with a small learning rate.</param>
public static BinaryClassificationGamTrainer GeneralizedAdditiveModels(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string exampleWeightColumnName = null,
int numberOfIterations = GamDefaults.NumberOfIterations,
int maximumBinCountPerFeature = GamDefaults.MaximumBinCountPerFeature,
double learningRate = GamDefaults.LearningRate)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new BinaryClassificationGamTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, numberOfIterations, learningRate, maximumBinCountPerFeature));
}
/// <summary>
/// Predict a target using a decision tree regression model trained with the <see cref="FastForestClassification"/>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="featureColumnName">The name of the feature column.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
/// <param name="numberOfTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="numberOfLeaves">The maximum number of leaves per decision tree.</param>
/// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
public static FastForestClassification FastForest(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string exampleWeightColumnName = null,
int numberOfLeaves = Defaults.NumberOfLeaves,
int numberOfTrees = Defaults.NumberOfTrees,
int minDatapointsInLeaves = Defaults.MinimumExampleCountPerLeaf)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FastForestClassification(env, labelColumnName, featureColumnName, exampleWeightColumnName, numberOfLeaves, numberOfTrees, minDatapointsInLeaves));
}
/// <summary>
/// Predict a target using a decision tree binary classification model trained with the <see cref="FastTreeBinaryClassificationTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="featureColumnName">The name of the feature column.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
/// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
/// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
public static FastTreeBinaryClassificationTrainer FastTree(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string exampleWeightColumnName = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
double learningRate = Defaults.LearningRates)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FastTreeBinaryClassificationTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, numLeaves, numTrees, minDatapointsInLeaves, learningRate));
}
/// <summary>
/// Predict a target using a decision tree binary classification model trained with the <see cref="LightGbmBinaryTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="featureColumnName">The name of the feature column.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
/// <param name="numberOfLeaves">The number of leaves to use.</param>
/// <param name="minimumExampleCountPerLeaf">The minimal number of data points allowed in a leaf of the tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="numberOfIterations">The number of iterations to use.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LightGbmBinaryTrainer LightGbm(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string exampleWeightColumnName = null,
int?numberOfLeaves = null,
int?minimumExampleCountPerLeaf = null,
double?learningRate = null,
int numberOfIterations = Options.Defaults.NumberOfIterations)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new LightGbmBinaryTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, numberOfLeaves, minimumExampleCountPerLeaf, learningRate, numberOfIterations));
}
/// <summary>
/// Predict a target using logistic regression trained with the <see cref="SgdBinaryTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The binary classificaiton catalog trainer object.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="featureColumnName">The name of the feature column.</param>
/// <param name="weightColumnName">The name for the example weight column.</param>
/// <param name="maxIterations">The maximum number of iterations; set to 1 to simulate online learning.</param>
/// <param name="initLearningRate">The initial learning rate used by SGD.</param>
/// <param name="l2Weight">The L2 regularization constant.</param>
public static SgdBinaryTrainer StochasticGradientDescent(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string weightColumnName = null,
int maxIterations = SgdBinaryTrainer.Options.Defaults.MaxIterations,
double initLearningRate = SgdBinaryTrainer.Options.Defaults.InitLearningRate,
float l2Weight = SgdBinaryTrainer.Options.Defaults.L2Weight)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new SgdBinaryTrainer(env, labelColumnName, featureColumnName, weightColumnName,
maxIterations, initLearningRate, l2Weight));
}
/// <summary>
/// Predict a target using a logistic regression model trained with the SDCA trainer.
/// The trained model can produce probablity by feeding the output value of the linear
/// function to a <see cref="PlattCalibrator"/>.
/// </summary>
/// <param name="catalog">The binary classification catalog trainer object.</param>
/// <param name="labelColumn">The labelColumn, or dependent variable.</param>
/// <param name="featureColumn">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="l2Const">The L2 regularization hyperparameter.</param>
/// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
/// <param name="maxIterations">The maximum number of passes to perform over the data.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static SdcaBinaryTrainer StochasticDualCoordinateAscent(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new SdcaBinaryTrainer(env, labelColumn, featureColumn, weights, l2Const, l1Threshold, maxIterations));
}
/// <summary>
/// Predict a target using a linear binary classification model trained with the <see cref="SymSgdClassificationTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="labelColumn">The labelColumn column.</param>
/// <param name="featureColumn">The features column.</param>
public static SymSgdClassificationTrainer SymbolicStochasticGradientDescent(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
var options = new SymSgdClassificationTrainer.Options
{
LabelColumn = labelColumn,
FeatureColumn = featureColumn,
};
return(new SymSgdClassificationTrainer(env, options));
}
/// <summary>
/// Predict a target using a linear binary classification model trained with the <see cref="Trainers.LogisticRegression"/> trainer.
/// </summary>
/// <param name="catalog">The binary classificaiton catalog trainer object.</param>
/// <param name="labelColumn">The label column name, or dependent variable.</param>
/// <param name="featureColumn">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="enforceNoNegativity">Enforce non-negative weights.</param>
/// <param name="l1Weight">Weight of L1 regularization term.</param>
/// <param name="l2Weight">Weight of L2 regularization term.</param>
/// <param name="memorySize">Memory size for <see cref="Trainers.LogisticRegression"/>. Low=faster, less accurate.</param>
/// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LogisticRegression LogisticRegression(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
float l1Weight = LROptions.Defaults.L1Weight,
float l2Weight = LROptions.Defaults.L2Weight,
float optimizationTolerance = LROptions.Defaults.OptTol,
int memorySize = LROptions.Defaults.MemorySize,
bool enforceNoNegativity = LROptions.Defaults.EnforceNonNegativity)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new LogisticRegression(env, labelColumn, featureColumn, weights, l1Weight, l2Weight, optimizationTolerance, memorySize, enforceNoNegativity));
}
/// <summary>
/// Predict a target using a linear binary classification model trained with the <see cref="SymbolicSgdTrainer"/>.
/// Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function.
/// The <see cref="SymbolicSgdTrainer"/> parallelizes SGD using <a href="https://www.microsoft.com/en-us/research/project/project-parade/#!symbolic-execution">symbolic execution</a>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="featureColumnName">The name of the feature column.</param>
/// <param name="numberOfIterations">Number of training iterations.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static SymbolicSgdTrainer SymbolicSgd(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
int numberOfIterations = SymbolicSgdTrainer.Defaults.NumberOfIterations)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
var options = new SymbolicSgdTrainer.Options
{
LabelColumnName = labelColumnName,
FeatureColumnName = featureColumnName,
};
return(new SymbolicSgdTrainer(env, options));
}
/// <summary>
/// Predict a target using a linear binary classification model trained with <see cref="AveragedPerceptronTrainer"/>.
/// </summary>
/// <param name="catalog">The binary classification catalog trainer object.</param>
/// <param name="labelColumn">The name of the label column, or dependent variable.</param>
/// <param name="featureColumn">The features, or independent variables.</param>
/// <param name="lossFunction">A custom <a href="tmpurl_loss">loss</a>. If <see langword="null"/>, hinge loss will be used resulting in max-margin averaged perceptron.</param>
/// <param name="learningRate"><a href="tmpurl_lr">Learning rate</a>.</param>
/// <param name="decreaseLearningRate">
/// <see langword="true" /> to decrease the <paramref name="learningRate"/> as iterations progress; otherwise, <see langword="false" />.
/// Default is <see langword="false" />.
/// </param>
/// <param name="l2RegularizerWeight">L2 weight for <a href='tmpurl_regularization'>regularization</a>.</param>
/// <param name="numIterations">Number of passes through the training dataset.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static AveragedPerceptronTrainer AveragedPerceptron(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
IClassificationLoss lossFunction = null,
float learningRate = AveragedLinearArguments.AveragedDefaultArgs.LearningRate,
bool decreaseLearningRate = AveragedLinearArguments.AveragedDefaultArgs.DecreaseLearningRate,
float l2RegularizerWeight = AveragedLinearArguments.AveragedDefaultArgs.L2RegularizerWeight,
int numIterations = AveragedLinearArguments.AveragedDefaultArgs.NumIterations)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new AveragedPerceptronTrainer(env, labelColumn, featureColumn, lossFunction ?? new LogLoss(), learningRate, decreaseLearningRate, l2RegularizerWeight, numIterations));
}
/// <summary>
/// Predict a target using a field-aware factorization machine algorithm.
/// </summary>
/// <param name="catalog">The binary classification catalog trainer object.</param>
/// <param name="featureColumnNames">The name(s) of the feature columns.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static FieldAwareFactorizationMachineTrainer FieldAwareFactorizationMachine(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string[] featureColumnNames,
string labelColumnName = DefaultColumnNames.Label,
string exampleWeightColumnName = null)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FieldAwareFactorizationMachineTrainer(env, featureColumnNames, labelColumnName, exampleWeightColumnName));
}
/// <summary>
/// Predict a target using the random binary classification model <see cref="RandomTrainer"/>.
/// </summary>
/// <remarks>
/// This trainer can be used as a baseline for other more sophisticated mdels.
/// </remarks>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static RandomTrainer Random(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog)
{
Contracts.CheckValue(catalog, nameof(catalog));
return(new RandomTrainer(CatalogUtils.GetEnvironment(catalog), new RandomTrainer.Options()));
}
/// <summary>
/// Predict a target using a field-aware factorization machine algorithm.
/// </summary>
/// <param name="catalog">The binary classification catalog trainer object.</param>
/// <param name="options">Advanced arguments to the algorithm.</param>
public static FieldAwareFactorizationMachineTrainer FieldAwareFactorizationMachine(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
FieldAwareFactorizationMachineTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FieldAwareFactorizationMachineTrainer(env, options));
}
/// <summary>
/// Predict a target using a field-aware factorization machine algorithm.
/// </summary>
/// <param name="catalog">The binary classification catalog trainer object.</param>
/// <param name="featureColumns">The features, or independent variables.</param>
/// <param name="labelColumn">The label, or dependent variable.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="advancedSettings">A delegate to set more settings.
/// The settings here will override the ones provided in the direct method signature,
/// if both are present and have different values.
/// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param>
public static FieldAwareFactorizationMachineTrainer FieldAwareFactorizationMachine(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string[] featureColumns,
string labelColumn = DefaultColumnNames.Label,
string weights = null,
Action <FieldAwareFactorizationMachineTrainer.Arguments> advancedSettings = null)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FieldAwareFactorizationMachineTrainer(env, featureColumns, labelColumn, weights, advancedSettings: advancedSettings));
}
