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[ML] Expand regression/classification hyperparameters (#67950)

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Expands data frame analytics regression and classification
analyses with the followin hyperparameters:

- alpha
- downsample_factor
- eta_growth_rate_per_tree
- max_optimization_rounds_per_hyperparameter
- soft_tree_depth_limit
- soft_tree_depth_tolerance
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Dimitris Athanasiou 2021-01-26 12:56:41 +02:00 • committed by GitHub
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commit 5c961c1c81
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14 changed files with 769 additions and 42 deletions
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12
docs/java-rest/high-level/ml/put-data-frame-analytics.asciidoc
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@ -128,6 +128,12 @@ include-tagged::{doc-tests-file}[{api}-classification]
<12> The number of top classes (or -1 which denotes all classes) to be reported in the results. Defaults to 2.
<13> Custom feature processors that will create new features for analysis from the included document
fields. Note, automatic categorical {ml-docs}/ml-feature-encoding.html[feature encoding] still occurs for all features.
<14> The alpha regularization parameter. A non-negative double.
<15> The growth rate of the shrinkage parameter. A double in [0.5, 2.0].
<16> The soft tree depth limit. A non-negative double.
<17> The soft tree depth tolerance. Controls how much the soft tree depth limit is respected. A double greater than or equal to 0.01.
<18> The amount by which to downsample the data for stochastic gradient estimates. A double in (0, 1.0].
<19> The maximum number of optimisation rounds we use for hyperparameter optimisation per parameter. An integer in [0, 20].
===== Regression
@ -152,6 +158,12 @@ include-tagged::{doc-tests-file}[{api}-regression]
<12> An optional parameter to the loss function.
<13> Custom feature processors that will create new features for analysis from the included document
fields. Note, automatic categorical {ml-docs}/ml-feature-encoding.html[feature encoding] still occurs for all features.
<14> The alpha regularization parameter. A non-negative double.
<15> The growth rate of the shrinkage parameter. A double in [0.5, 2.0].
<16> The soft tree depth limit. A non-negative double.
<17> The soft tree depth tolerance. Controls how much the soft tree depth limit is respected. A double greater than or equal to 0.01.
<18> The amount by which to downsample the data for stochastic gradient estimates. A double in (0, 1.0].
<19> The maximum number of optimisation rounds we use for hyperparameter optimisation per parameter. An integer in [0, 20].
==== Analyzed fields