## Uses of Interface
org.apache.ignite.ml.composition.boosting.loss.Loss

- Packages that use Loss
  Package | Description
  --- | ---
  org.apache.ignite.ml.composition.boosting | Contains Gradient Boosting regression and classification abstract classes allowing regressor type selecting in child classes.
  org.apache.ignite.ml.composition.boosting.convergence | Package contains implementation of convergence checking algorithms for gradient boosting.
  org.apache.ignite.ml.composition.boosting.convergence.mean | Contains implementation of convergence checking computer by mean of absolute value of errors in dataset.
  org.apache.ignite.ml.composition.boosting.convergence.median | Contains implementation of convergence checking computer by median of medians of errors in dataset.
  org.apache.ignite.ml.composition.boosting.convergence.simple | Contains implementation of Stub for convergence checking.
  org.apache.ignite.ml.composition.boosting.loss | Contains loss functions for Gradient Boosting algorithms.

- - ### Uses of Loss in org.apache.ignite.ml.composition.boosting

    Fields in org.apache.ignite.ml.composition.boosting declared as Loss
    Modifier and Type | Field and Description
    --- | ---
    `protected Loss` | GDBTrainer.`loss` Loss function.
    `protected Loss` | GDBLearningStrategy.`loss` Loss of gradient.

    Methods in org.apache.ignite.ml.composition.boosting with parameters of type Loss
    Modifier and Type | Method and Description
    --- | ---
    `GDBLearningStrategy` | GDBLearningStrategy.`withLossGradient(Loss loss)` Loss function.

    Constructors in org.apache.ignite.ml.composition.boosting with parameters of type Loss
    Constructor and Description
    `GDBBinaryClassifierTrainer(double gradStepSize, Integer cntOfIterations, Loss loss)` Constructs instance of GDBBinaryClassifierTrainer.
    `GDBTrainer(double gradStepSize, Integer cntOfIterations, Loss loss)` Constructs GDBTrainer instance.

  - ### Uses of Loss in org.apache.ignite.ml.composition.boosting.convergence

    Methods in org.apache.ignite.ml.composition.boosting.convergence with parameters of type Loss
    Modifier and Type | Method and Description
    --- | ---
    `abstract <K,V> ConvergenceChecker<K,V>` | ConvergenceCheckerFactory.`create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> vectorizer)` Create an instance of ConvergenceChecker.

    Constructors in org.apache.ignite.ml.composition.boosting.convergence with parameters of type Loss
    Constructor and Description
    `ConvergenceChecker(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor, double precision)` Constructs an instance of ConvergenceChecker.

  - ### Uses of Loss in org.apache.ignite.ml.composition.boosting.convergence.mean

    Methods in org.apache.ignite.ml.composition.boosting.convergence.mean with parameters of type Loss
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> ConvergenceChecker<K,V>` | MeanAbsValueConvergenceCheckerFactory.`create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Create an instance of ConvergenceChecker.

    Constructors in org.apache.ignite.ml.composition.boosting.convergence.mean with parameters of type Loss
    Constructor and Description
    `MeanAbsValueConvergenceChecker(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor, double precision)` Creates an instance of MeanAbsValueConvergenceChecker.

  - ### Uses of Loss in org.apache.ignite.ml.composition.boosting.convergence.median

    Methods in org.apache.ignite.ml.composition.boosting.convergence.median with parameters of type Loss
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> ConvergenceChecker<K,V>` | MedianOfMedianConvergenceCheckerFactory.`create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Create an instance of ConvergenceChecker.

    Constructors in org.apache.ignite.ml.composition.boosting.convergence.median with parameters of type Loss
    Constructor and Description
    `MedianOfMedianConvergenceChecker(long sampleSize, IgniteFunction<Double,Double> lblMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor, double precision)` Creates an instance of MedianOfMedianConvergenceChecker.

  - ### Uses of Loss in org.apache.ignite.ml.composition.boosting.convergence.simple

    Methods in org.apache.ignite.ml.composition.boosting.convergence.simple with parameters of type Loss
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> ConvergenceChecker<K,V>` | ConvergenceCheckerStubFactory.`create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Create an instance of ConvergenceChecker.

    Constructors in org.apache.ignite.ml.composition.boosting.convergence.simple with parameters of type Loss
    Constructor and Description
    `ConvergenceCheckerStub(long sampleSize, IgniteFunction externalLbToInternalMapping, Loss loss, DatasetBuilder datasetBuilder, Preprocessor<K,V> preprocessor, double precision)` Creates an instance of ConvergenceCheckerStub.

  - ### Uses of Loss in org.apache.ignite.ml.composition.boosting.loss

    Classes in org.apache.ignite.ml.composition.boosting.loss that implement Loss
    Modifier and Type | Class and Description
    --- | ---
    `class` | `LogLoss` Logistic regression loss function.
    `class` | `SquaredError` Represent error function as E(label, modelAnswer) = 1/N * (label - prediction)^2

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