parallelTreeCount
TypeScriptget parallelTreeCount(): numberSwift declarationvar parallelTreeCount: IntThe number of parallel trees constructed during each iteration. Use a value greater than one to train a boosted random forest.
struct · iOS 16.0+ · SDK 27.1
struct BoostedTreeConfigurationA boosted tree configuration.
get parallelTreeCount(): numberSwift declarationvar parallelTreeCount: IntThe number of parallel trees constructed during each iteration. Use a value greater than one to train a boosted random forest.
set parallelTreeCount(value: number)Swift declarationvar parallelTreeCount: IntThe number of parallel trees constructed during each iteration. Use a value greater than one to train a boosted random forest.
get randomSeed(): numberSwift declarationvar randomSeed: IntA seed to generate reproducible results from random operations such as column and row subsampling.
set randomSeed(value: number)Swift declarationvar randomSeed: IntA seed to generate reproducible results from random operations such as column and row subsampling.
get learningRate(): numberSwift declarationvar learningRate: Double { get set }The learning rate. The learning rate controls the step size shrinkage. A smaller learning rate makes the learning process more conservative. Must be in the range [0, 1]. Defaults to 0.3.
set learningRate(value: number)Swift declarationvar learningRate: Double { get set }The learning rate. The learning rate controls the step size shrinkage. A smaller learning rate makes the learning process more conservative. Must be in the range [0, 1]. Defaults to 0.3.
get maximumDepth(): numberSwift declarationvar maximumDepth: IntMaximum tree depth. For best results use a value between 4 and 8. Must be at least 1. Defaults to 6.
set maximumDepth(value: number)Swift declarationvar maximumDepth: IntMaximum tree depth. For best results use a value between 4 and 8. Must be at least 1. Defaults to 6.
get rowSubsample(): numberSwift declarationvar rowSubsample: DoubleSubsample ratio of the training set in each iteration of tree construction. This is called the bagging trick and can usually help prevent overfitting. Setting this to a value of 0.5 results in the model randomly sampling half of the examples (rows) to grow each tree. Must be in the range (0, 1).
set rowSubsample(value: number)Swift declarationvar rowSubsample: DoubleSubsample ratio of the training set in each iteration of tree construction. This is called the bagging trick and can usually help prevent overfitting. Setting this to a value of 0.5 results in the model randomly sampling half of the examples (rows) to grow each tree. Must be in the range (0, 1).
get columnSubsample(): numberSwift declarationvar columnSubsample: DoubleSubsample ratio of the columns in each iteration of tree construction. Like row subsample, this can also help prevent model overfitting. Setting this to a value of 0.5 results in the model randomly sampling half of the columns to grow each tree. Must be in the range (0, 1).
set columnSubsample(value: number)Swift declarationvar columnSubsample: DoubleSubsample ratio of the columns in each iteration of tree construction. Like row subsample, this can also help prevent model overfitting. Setting this to a value of 0.5 results in the model randomly sampling half of the columns to grow each tree. Must be in the range (0, 1).
get maximumIterations(): numberSwift declarationvar maximumIterations: IntMaximum number of iterations.
set maximumIterations(value: number)Swift declarationvar maximumIterations: IntMaximum number of iterations.
get minimumChildWeight(): numberSwift declarationvar minimumChildWeight: DoubleThe minimum weight of each leaf node. Larger values result in more conservative tree learning and help prevent overfitting. If the tree learning algorithm results in a leaf node with the sum of instance weights less than this value, tree building will terminate. Defaults to 0.1
set minimumChildWeight(value: number)Swift declarationvar minimumChildWeight: DoubleThe minimum weight of each leaf node. Larger values result in more conservative tree learning and help prevent overfitting. If the tree learning algorithm results in a leaf node with the sum of instance weights less than this value, tree building will terminate. Defaults to 0.1
get minimumLossReduction(): numberSwift declarationvar minimumLossReduction: DoubleMinimum loss reduction required to further split a node during the tree learning phase. Larger values can help prevent overfitting by avoiding splits that do not sufficiently reduce the loss function. Defaults to 0.
set minimumLossReduction(value: number)Swift declarationvar minimumLossReduction: DoubleMinimum loss reduction required to further split a node during the tree learning phase. Larger values can help prevent overfitting by avoiding splits that do not sufficiently reduce the loss function. Defaults to 0.
get earlyStoppingIterationCount(): (number) | nullSwift declarationvar earlyStoppingIterationCount: Int?Stops training after this number of iterations where the validation metric does not improve. Validation data must be specified for an early stop.
set earlyStoppingIterationCount(value: (number) | null)Swift declarationvar earlyStoppingIterationCount: Int?Stops training after this number of iterations where the validation metric does not improve. Validation data must be specified for an early stop.
get stepSize(): numberSwift declarationvar stepSize: Double { get set }The step size shrinking.
set stepSize(value: number)Swift declarationvar stepSize: Double { get set }The step size shrinking.
static init(): BoostedTreeConfigurationSwift declarationinit()Creates a default boosted tree configuration.
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