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+[role="xpack"]
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+[testenv="basic"]
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+[[put-inference]]
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+=== Create {infer} trained model API
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+[subs="attributes"]
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+++++
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+<titleabbrev>Create {infer} trained model</titleabbrev>
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+++++
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+
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+Creates an {infer} trained model.
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+
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+experimental[]
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+
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+
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+[[ml-put-inference-request]]
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+==== {api-request-title}
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+
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+`PUT _ml/inference/<model_id>`
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+
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+
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+[[ml-put-inference-prereq]]
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+==== {api-prereq-title}
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+
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+If the {es} {security-features} are enabled, you must have the following
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+built-in roles and privileges:
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+
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+* `machine_learning_admin`
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+
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+For more information, see <<security-privileges>> and <<built-in-roles>>.
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+
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+
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+[[ml-put-inference-desc]]
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+==== {api-description-title}
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+
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+The create {infer} trained model API enables you to supply a trained model that
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+is not created by {dfanalytics}.
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+
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+
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+[[ml-put-inference-path-params]]
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+==== {api-path-parms-title}
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+
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+`<model_id>`::
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+(Required, string)
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+include::{docdir}/ml/ml-shared.asciidoc[tag=model-id]
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+
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+
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+[[ml-put-inference-request-body]]
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+==== {api-request-body-title}
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+
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+`compressed_definition`::
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+(Required, string)
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+The compressed (GZipped and Base64 encoded) {infer} definition of the model.
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+If `compressed_definition` is specified, then `definition` cannot be specified.
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+
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+`definition`::
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+(Required, object)
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+The {infer} definition for the model. If `definition` is specified, then
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+`compressed_definition` cannot be specified.
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+
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+`definition`.`preprocessors`:::
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+(Optional, object)
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+Collection of preprocessors. See <<ml-put-inference-preprocessors>> for the full
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+list of available preprocessors.
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+
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+`definition`.`trained_model`:::
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+(Required, object)
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+The definition of the trained model. See <<ml-put-inference-trained-model>> for
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+details.
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+
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+`description`::
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+(Optional, string)
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+A human-readable description of the {infer} trained model.
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+
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+`input`::
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+(Required, object)
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+The input field names for the model definition.
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+
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+`input`.`field_names`:::
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+(Required, string)
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+An array of input field names for the model.
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+
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+`metadata`::
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+(Optional, object)
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+An object map that contains metadata about the model.
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+
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+`tags`::
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+(Optional, string)
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+An array of tags to organize the model.
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+
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+
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+[[ml-put-inference-preprocessors]]
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+===== {infer-cap} preprocessor definitions
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+
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+`frequency_encoding`::
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+(Required, object)
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+Defines a frequency encoding for a field.
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+
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+`frequency_encoding`.`field`:::
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+(Required, string)
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+The field name to encode.
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+
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+`frequency_encoding`.`feature_name`:::
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+(Required, string)
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+The name of the resulting feature.
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+
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+`frequency_encoding`.`frequency_map`:::
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+(Required, object map of string:double)
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+Object that maps the field value to the frequency encoded value.
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+
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+`one_hot_encoding`::
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+(Required, object)
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+Defines a one hot encoding map for a field.
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+
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+`one_hot_encoding`.`field`:::
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+(Required, string)
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+The field name to encode.
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+
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+`one_hot_encoding`.`hot_map`:::
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+(Required, object map of strings)
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+String map of "field_value: one_hot_column_name".
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+
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+`target_mean_encoding`::
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+(Required, object)
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+Defines a target mean encoding for a field.
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+
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+`target_mean_encoding`.`field`:::
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+(Required, string)
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+The field name to encode.
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+
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+`target_mean_encoding`.`feature_name`:::
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+(Required, string)
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+The name of the resulting feature.
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+
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+`target_mean_encoding`.`target_map`:::
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+(Required, object map of string:double)
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+Object that maps the field value to the target mean value.
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+
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+`target_mean_encoding`.`default_value`:::
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+(Required, double)
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+The feature value if the field value is not in the `target_map`.
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+
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+See <<ml-put-inference-preprocessor-example>> for more details.
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+
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+
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+[[ml-put-inference-trained-model]]
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+===== {infer-cap} trained model definitions
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+
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+`tree`::
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+(Required, object)
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+The definition for a binary decision tree.
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+
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+`tree`.`feature_names`:::
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+(Required, string)
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+Features expected by the tree, in their expected order.
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+
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+`tree`.`tree_structure`:::
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+(Required, object)
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+An array of `tree_node` objects. The nodes must be in ordinal order by their
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+`tree_node.node_index` value.
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+
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+`tree`.`classification_labels`:::
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+(Optional, string) An array of classification labels (used for
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+`classification`).
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+
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+`tree`.`target_type`:::
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+(Required, string)
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+String indicating the model target type; `regression` or `classification`.
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+
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+There are two major types of nodes: leaf nodes and not-leaf nodes.
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+
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+* Leaf nodes only need `node_index` and `leaf_value` defined.
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+* All other nodes need `split_feature`, `left_child`, `right_child`,
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+ `threshold`, `decision_type`, and `default_left` defined.
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+
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+
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+
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+`tree_node`::
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+(Required, object)
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+The definition of a node in a tree.
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+
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+`tree_node`.`decision_type`:::
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+(Optional, string)
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+Indicates the positive value (in other words, when to choose the left node)
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+decision type. Supported `lt`, `lte`, `gt`, `gte`. Defaults to `lte`.
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+
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+`tree_node`.`threshold`:::
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+(Optional, double)
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+The decision threshold with which to compare the feature value.
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+
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+`tree_node`.`left_child`:::
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+(Optional, integer)
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+The index of the left child.
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+
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+`tree_node`.`right_child`:::
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+(Optional, integer)
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+The index of the right child.
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+
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+`tree_node`.`default_left`:::
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+(Optional, boolean)
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+Indicates whether to default to the left when the feature is missing. Defaults
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+to `true`.
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+
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+`tree_node`.`split_feature`:::
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+(Optional, integer)
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+The index of the feature value in the feature array.
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+
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+`tree_node`.`node_index`:::
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+(Integer)
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+The index of the current node.
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+
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+`tree_node`.`split_gain`:::
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+(Optional, double) The information gain from the split.
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+
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+`tree_node`.`leaf_value`:::
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+(Optional, double)
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+The leaf value of the of the node, if the value is a leaf (in other words, no
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+children).
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+
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+`ensemble`::
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+(Optional, object)
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+The definition for an ensemble model.
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+
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+`ensemble`.`feature_names`:::
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+(Required, string)
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+Features expected by the ensemble, in their expected order.
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+
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+`ensemble`.`trained_models`:::
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+(Required, object)
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+An array of `trained_model` objects. Supported trained models are `tree` and
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+`ensemble`.
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+
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+`ensemble`.`classification_labels`:::
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+(Optional, string)
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+An array of classification labels.
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+
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+`ensemble`.`target_type`:::
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+(Required, string)
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+String indicating the model target type; `regression` or `classification.`
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+
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+`ensemble`.`aggregate_output`:::
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+(Required, object)
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+An aggregated output object that defines how to aggregate the outputs of the
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+`trained_models`. Supported objects are `weighted_mode`, `weighted_sum`, and
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+`logistic_regression`.
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+
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+See <<ml-put-inference-model-example>> for more details.
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+
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+
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+[[ml-put-inference-aggregated-output]]
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+===== Aggregated output types
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+
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+`logistic_regression`::
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+(Optional, object)
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+This `aggregated_output` type works with binary classification (classification
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+for values [0, 1]). It multiplies the outputs (in the case of the `ensemble`
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+model, the inference model values) by the supplied `weights`. The resulting
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+vector is summed and passed to a
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+https://en.wikipedia.org/wiki/Sigmoid_function[`sigmoid` function]. The result
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+of the `sigmoid` function is considered the probability of class 1 (`P_1`),
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+consequently, the probability of class 0 is `1 - P_1`. The class with the
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+highest probability (either 0 or 1) is then returned. For more information about
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+logistic regression, see
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+https://en.wikipedia.org/wiki/Logistic_regression[this wiki article].
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+
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+`logistic_regression`.`weights`:::
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+(Required, double)
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+The weights to multiply by the input values (the inference values of the trained
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+models).
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+
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+`weighted_sum`::
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+(Optional, object)
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+This `aggregated_output` type works with regression. The weighted sum of the
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+input values.
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+
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+`weighted_sum`.`weights`:::
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+(Required, double)
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+The weights to multiply by the input values (the inference values of the trained
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+models).
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+
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+`weighted_mode`::
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+(Optional, object)
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+This `aggregated_output` type works with regression or classification. It takes
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+a weighted vote of the input values. The most common input value (taking the
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+weights into account) is returned.
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+
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+`weighted_mode`.`weights`:::
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+(Required, double)
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+The weights to multiply by the input values (the inference values of the trained
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+models).
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+
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+See <<ml-put-inference-aggregated-output-example>> for more details.
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+
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+
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+[[ml-put-inference-example]]
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+==== {api-examples-title}
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+
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+[[ml-put-inference-preprocessor-example]]
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+===== Preprocessor examples
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+
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+The example below shows a `frequency_encoding` preprocessor object:
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+
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+[source,js]
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+----------------------------------
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+{
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+ "frequency_encoding":{
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+ "field":"FlightDelayType",
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+ "feature_name":"FlightDelayType_frequency",
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+ "frequency_map":{
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+ "Carrier Delay":0.6007414737092798,
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+ "NAS Delay":0.6007414737092798,
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+ "Weather Delay":0.024573576178086153,
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+ "Security Delay":0.02476631010889467,
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+ "No Delay":0.6007414737092798,
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+ "Late Aircraft Delay":0.6007414737092798
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+ }
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+ }
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+}
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+----------------------------------
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+//NOTCONSOLE
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+
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+
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+The next example shows a `one_hot_encoding` preprocessor object:
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+
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+[source,js]
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+----------------------------------
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+{
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+ "one_hot_encoding":{
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+ "field":"FlightDelayType",
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+ "hot_map":{
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+ "Carrier Delay":"FlightDelayType_Carrier Delay",
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+ "NAS Delay":"FlightDelayType_NAS Delay",
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+ "No Delay":"FlightDelayType_No Delay",
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+ "Late Aircraft Delay":"FlightDelayType_Late Aircraft Delay"
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+ }
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+ }
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+}
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+----------------------------------
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+//NOTCONSOLE
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+
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+
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+This example shows a `target_mean_encoding` preprocessor object:
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+
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+[source,js]
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+----------------------------------
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+{
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+ "target_mean_encoding":{
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+ "field":"FlightDelayType",
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+ "feature_name":"FlightDelayType_targetmean",
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+ "target_map":{
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+ "Carrier Delay":39.97465788139886,
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+ "NAS Delay":39.97465788139886,
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+ "Security Delay":203.171206225681,
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+ "Weather Delay":187.64705882352948,
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+ "No Delay":39.97465788139886,
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+ "Late Aircraft Delay":39.97465788139886
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+ },
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+ "default_value":158.17995752420433
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+ }
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+}
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+----------------------------------
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+//NOTCONSOLE
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+
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+
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+[[ml-put-inference-model-example]]
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+===== Model examples
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+
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+The first example shows a `trained_model` object:
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+
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+[source,js]
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+----------------------------------
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+{
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+ "tree":{
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+ "feature_names":[
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+ "DistanceKilometers",
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+ "FlightTimeMin",
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+ "FlightDelayType_NAS Delay",
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+ "Origin_targetmean",
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+ "DestRegion_targetmean",
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+ "DestCityName_targetmean",
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+ "OriginAirportID_targetmean",
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+ "OriginCityName_frequency",
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+ "DistanceMiles",
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+ "FlightDelayType_Late Aircraft Delay"
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+ ],
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+ "tree_structure":[
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+ {
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+ "decision_type":"lt",
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+ "threshold":9069.33437193022,
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+ "split_feature":0,
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+ "split_gain":4112.094574306927,
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+ "node_index":0,
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+ "default_left":true,
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+ "left_child":1,
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+ "right_child":2
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+ },
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+ ...
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+ {
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+ "node_index":9,
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+ "leaf_value":-27.68987349695448
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+ },
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+ ...
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+ ],
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+ "target_type":"regression"
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+ }
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+}
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+----------------------------------
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+//NOTCONSOLE
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+
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+
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+The following example shows an `ensemble` model object:
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+
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+[source,js]
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+----------------------------------
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+"ensemble":{
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+ "feature_names":[
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+ ...
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+ ],
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+ "trained_models":[
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+ {
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+ "tree":{
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+ "feature_names":[],
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+ "tree_structure":[
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+ {
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+ "decision_type":"lte",
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+ "node_index":0,
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+ "leaf_value":47.64069875778043,
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+ "default_left":false
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+ }
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+ ],
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+ "target_type":"regression"
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+ }
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+ },
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+ ...
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+ ],
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+ "aggregate_output":{
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+ "weighted_sum":{
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+ "weights":[
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+ ...
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+ ]
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+ }
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+ },
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+ "target_type":"regression"
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+}
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|
|
+----------------------------------
|
|
|
+//NOTCONSOLE
|
|
|
+
|
|
|
+
|
|
|
+[[ml-put-inference-aggregated-output-example]]
|
|
|
+===== Aggregated output example
|
|
|
+
|
|
|
+Example of a `logistic_regression` object:
|
|
|
+
|
|
|
+[source,js]
|
|
|
+----------------------------------
|
|
|
+"aggregate_output" : {
|
|
|
+ "logistic_regression" : {
|
|
|
+ "weights" : [2.0, 1.0, .5, -1.0, 5.0, 1.0, 1.0]
|
|
|
+ }
|
|
|
+}
|
|
|
+----------------------------------
|
|
|
+//NOTCONSOLE
|
|
|
+
|
|
|
+
|
|
|
+Example of a `weighted_sum` object:
|
|
|
+
|
|
|
+[source,js]
|
|
|
+----------------------------------
|
|
|
+"aggregate_output" : {
|
|
|
+ "weighted_sum" : {
|
|
|
+ "weights" : [1.0, -1.0, .5, 1.0, 5.0]
|
|
|
+ }
|
|
|
+}
|
|
|
+----------------------------------
|
|
|
+//NOTCONSOLE
|
|
|
+
|
|
|
+
|
|
|
+Example of a `weighted_mode` object:
|
|
|
+
|
|
|
+[source,js]
|
|
|
+----------------------------------
|
|
|
+"aggregate_output" : {
|
|
|
+ "weighted_mode" : {
|
|
|
+ "weights" : [1.0, 1.0, 1.0, 1.0, 1.0]
|
|
|
+ }
|
|
|
+}
|
|
|
+----------------------------------
|
|
|
+//NOTCONSOLE
|
|
|
+
|
|
|
+
|
|
|
+[[ml-put-inference-json-schema]]
|
|
|
+===== {infer-cap} JSON schema
|
|
|
+
|
|
|
+For the full JSON schema of model {infer},
|
|
|
+https://github.com/elastic/ml-json-schemas[click here].
|