2019-07-05 07:34:05 -04:00
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[role="xpack"]
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[testenv="platinum"]
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[[evaluate-dfanalytics]]
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=== Evaluate {dfanalytics} API
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[subs="attributes"]
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++++
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<titleabbrev>Evaluate {dfanalytics}</titleabbrev>
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++++
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2019-07-11 12:05:05 -04:00
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Evaluates the {dfanalytics} for an annotated index.
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2019-07-05 07:34:05 -04:00
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2019-07-11 12:05:05 -04:00
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experimental[]
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2019-07-05 07:34:05 -04:00
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[[ml-evaluate-dfanalytics-request]]
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==== {api-request-title}
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`POST _ml/data_frame/_evaluate`
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[[ml-evaluate-dfanalytics-prereq]]
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==== {api-prereq-title}
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* You must have `monitor_ml` privilege to use this API. For more
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information, see <<security-privileges>> and <<built-in-roles>>.
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2019-07-11 12:05:05 -04:00
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[[ml-evaluate-dfanalytics-desc]]
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==== {api-description-title}
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2019-09-19 03:10:11 -04:00
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The API packages together commonly used evaluation metrics for various types of
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machine learning features. This has been designed for use on indexes created by
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{dfanalytics}. Evaluation requires both a ground truth field and an analytics
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result field to be present.
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2019-07-11 12:05:05 -04:00
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2019-07-05 07:34:05 -04:00
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[[ml-evaluate-dfanalytics-request-body]]
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==== {api-request-body-title}
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2019-07-12 11:26:31 -04:00
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`index`::
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(Required, object) Defines the `index` in which the evaluation will be
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performed.
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`query`::
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(Optional, object) A query clause that retrieves a subset of data from the
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source index. See <<query-dsl>>.
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2019-07-12 11:26:31 -04:00
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`evaluation`::
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(Required, object) Defines the type of evaluation you want to perform. See
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<<ml-evaluate-dfanalytics-resources>>.
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+
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--
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Available evaluation types:
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* `binary_soft_classification`
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* `regression`
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--
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2019-07-12 11:26:31 -04:00
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////
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[[ml-evaluate-dfanalytics-results]]
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==== {api-response-body-title}
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`binary_soft_classification`::
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(object) If you chose to do binary soft classification, the API returns the
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following evaluation metrics:
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`auc_roc`::: TBD
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`confusion_matrix`::: TBD
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`precision`::: TBD
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`recall`::: TBD
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2019-07-12 11:26:31 -04:00
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////
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[[ml-evaluate-dfanalytics-example]]
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==== {api-examples-title}
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2019-09-19 03:10:11 -04:00
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===== Binary soft classification
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2019-09-09 12:35:50 -04:00
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[source,console]
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--------------------------------------------------
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POST _ml/data_frame/_evaluate
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{
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"index": "my_analytics_dest_index",
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"evaluation": {
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"binary_soft_classification": {
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"actual_field": "is_outlier",
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"predicted_probability_field": "ml.outlier_score"
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}
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}
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}
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--------------------------------------------------
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// TEST[skip:TBD]
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The API returns the following results:
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[source,console-result]
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----
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{
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"binary_soft_classification": {
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"auc_roc": {
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"score": 0.92584757746414444
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},
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"confusion_matrix": {
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"0.25": {
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"tp": 5,
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"fp": 9,
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"tn": 204,
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"fn": 5
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},
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"0.5": {
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"tp": 1,
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"fp": 5,
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"tn": 208,
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"fn": 9
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},
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"0.75": {
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"tp": 0,
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"fp": 4,
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"tn": 209,
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"fn": 10
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}
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},
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"precision": {
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"0.25": 0.35714285714285715,
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"0.5": 0.16666666666666666,
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"0.75": 0
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},
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"recall": {
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"0.25": 0.5,
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"0.5": 0.1,
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"0.75": 0
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}
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}
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}
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----
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===== {regression-cap}
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[source,console]
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--------------------------------------------------
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POST _ml/data_frame/_evaluate
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{
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"index": "house_price_predictions", <1>
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"query": {
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"bool": {
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"filter": [
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{ "term": { "ml.is_training": false } } <2>
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]
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}
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},
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"evaluation": {
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"regression": {
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"actual_field": "price", <3>
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"predicted_field": "ml.price_prediction", <4>
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"metrics": {
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"r_squared": {},
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"mean_squared_error": {}
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}
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}
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}
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}
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--------------------------------------------------
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// TEST[skip:TBD]
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<1> The output destination index from a {dfanalytics} {reganalysis}.
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<2> In this example, a test/train split (`training_percent`) was defined for the
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{reganalysis}. This query limits evaluation to be performed on the test split
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only.
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<3> The ground truth value for the actual house price. This is required in order
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to evaluate results.
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<4> The predicted value for house price calculated by the {reganalysis}.
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2019-10-02 04:26:20 -04:00
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The following example calculates the training error:
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[source,console]
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--------------------------------------------------
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POST _ml/data_frame/_evaluate
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{
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"index": "student_performance_mathematics_reg",
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"query": {
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"term": {
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"ml.is_training": {
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"value": true <1>
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}
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}
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},
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"evaluation": {
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"regression": {
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"actual_field": "G3", <2>
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"predicted_field": "ml.G3_prediction", <3>
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"metrics": {
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"r_squared": {},
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"mean_squared_error": {}
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}
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}
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}
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}
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--------------------------------------------------
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// TEST[skip:TBD]
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<1> In this example, a test/train split (`training_percent`) was defined for the
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{reganalysis}. This query limits evaluation to be performed on the train split
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only. It means that a training error will be calculated.
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<2> The field that contains the ground truth value for the actual student
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performance. This is required in order to evaluate results.
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<3> The field that contains the predicted value for student performance
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calculated by the {reganalysis}.
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The next example calculates the testing error. The only difference compared with
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the previous example is that `ml.is_training` is set to `false` this time, so
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the query excludes the train split from the evaluation.
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[source,console]
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--------------------------------------------------
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POST _ml/data_frame/_evaluate
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{
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"index": "student_performance_mathematics_reg",
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"query": {
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"term": {
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"ml.is_training": {
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"value": false <1>
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}
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}
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},
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"evaluation": {
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"regression": {
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"actual_field": "G3", <2>
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"predicted_field": "ml.G3_prediction", <3>
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"metrics": {
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"r_squared": {},
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"mean_squared_error": {}
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}
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}
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}
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}
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--------------------------------------------------
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// TEST[skip:TBD]
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<1> In this example, a test/train split (`training_percent`) was defined for the
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{reganalysis}. This query limits evaluation to be performed on the test split
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only. It means that a testing error will be calculated.
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<2> The field that contains the ground truth value for the actual student
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performance. This is required in order to evaluate results.
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<3> The field that contains the predicted value for student performance
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calculated by the {reganalysis}.
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