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| Package | Description |
|---|---|
| org.apache.ignite.ml.composition.boosting |
Contains Gradient Boosting regression and classification abstract classes
allowing regressor type selecting in child classes.
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| org.apache.ignite.ml.composition.boosting.convergence |
Package contains implementation of convergency checking algorithms for gradient boosting.
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| org.apache.ignite.ml.composition.boosting.convergence.mean |
Contains implementation of convergence checking computer by mean of absolute value of errors in dataset.
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| org.apache.ignite.ml.composition.boosting.convergence.median |
Contains implementation of convergence checking computer by median of medians of errors in dataset.
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| org.apache.ignite.ml.composition.boosting.convergence.simple |
Contains implementation of Stub for convergence checking.
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| org.apache.ignite.ml.composition.boosting.loss |
Contains loss functions for Gradient Boosting algorithms.
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| Modifier and Type | Field and Description |
|---|---|
protected Loss |
GDBLearningStrategy.loss
Loss of gradient.
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protected Loss |
GDBTrainer.loss
Loss function.
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| Modifier and Type | Method and Description |
|---|---|
GDBLearningStrategy |
GDBLearningStrategy.withLossGradient(Loss loss)
Loss function.
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| Constructor and Description |
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GDBBinaryClassifierTrainer(double gradStepSize,
Integer cntOfIterations,
Loss loss)
Constructs instance of GDBBinaryClassifierTrainer.
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GDBTrainer(double gradStepSize,
Integer cntOfIterations,
Loss loss)
Constructs GDBTrainer instance.
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| 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.
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| Constructor and Description |
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ConvergenceChecker(long sampleSize,
IgniteFunction<Double,Double> externalLbToInternalMapping,
Loss loss,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor,
double precision)
Constructs an instance of ConvergenceChecker.
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| 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.
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| Constructor and Description |
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MeanAbsValueConvergenceChecker(long sampleSize,
IgniteFunction<Double,Double> externalLbToInternalMapping,
Loss loss,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor,
double precision)
Creates an instance of MeanAbsValueConvergenceChecker.
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| 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.
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| Constructor and Description |
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MedianOfMedianConvergenceChecker(long sampleSize,
IgniteFunction<Double,Double> lblMapping,
Loss loss,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor,
double precision)
Creates an instance of MedianOfMedianConvergenceChecker.
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| 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.
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| Constructor and Description |
|---|
ConvergenceCheckerStub(long sampleSize,
IgniteFunction externalLbToInternalMapping,
Loss loss,
DatasetBuilder datasetBuilder,
Preprocessor<K,V> preprocessor,
double precision)
Creates an instance of ConvergenceCheckerStub.
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| Modifier and Type | Class and Description |
|---|---|
class |
LogLoss
Logistic regression loss function.
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class |
SquaredError
Represent error function as E(label, modelAnswer) = 1/N * (label - prediction)^2
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GridGain In-Memory Computing Platform : ver. 8.9.14 Release Date : November 5 2024