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@@ -81,8 +81,8 @@ one of the following types of analysis: {classification}, {oldetection}, or
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//Begin classification
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`classification`:::
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(Required^*^, object)
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-The configuration information necessary to perform {classification}:
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-// {ml-docs}/ml-dfa-classification.html[{classification}].
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+The configuration information necessary to perform
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+{ml-docs}/ml-dfa-classification.html[{classification}].
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+
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TIP: Advanced parameters are for fine-tuning {classanalysis}. They are set
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automatically by hyperparameter optimization to give the minimum validation
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@@ -263,8 +263,9 @@ a large number of categories, there could be a significant effect on the size of
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+
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--
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NOTE: To use the
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-AUC ROC evaluation method, `num_top_classes` must be set to `-1` or a value
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-greater than or equal to the total number of categories.
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+{ml-docs}/ml-dfa-classification.html#ml-dfanalytics-class-aucroc[AUC ROC]
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+evaluation method, `num_top_classes` must be set to `-1` or a value greater than
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+or equal to the total number of categories.
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--
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@@ -332,8 +333,8 @@ include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=standardization-enabled]
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//Begin regression
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`regression`:::
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(Required^*^, object)
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-The configuration information necessary to perform {regression}:
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-// {ml-docs}/ml-dfa-regression.html[{regression}].
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+The configuration information necessary to perform
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+{ml-docs}/ml-dfa-regression.html[{regression}].
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+
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TIP: Advanced parameters are for fine-tuning {reganalysis}. They are set
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automatically by hyperparameter optimization to give the minimum validation
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@@ -391,9 +392,9 @@ include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=lambda]
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The loss function used during {regression}. Available options are `mse` (mean
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squared error), `msle` (mean squared logarithmic error), `huber` (Pseudo-Huber
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loss). Defaults to `mse`.
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-// Refer to
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-// {ml-docs}/ml-dfa-regression.html#dfa-regression-lossfunction[Loss functions for {regression} analyses]
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-// to learn more.
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+Refer to
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+{ml-docs}/ml-dfa-regression.html#dfa-regression-lossfunction[Loss functions for {regression} analyses]
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+to learn more.
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`loss_function_parameter`::::
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(Optional, double)
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