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Merge doc-examples branch (#2759)
* Add machine learning API examples (#2452) * Add examples for transform APIs (#2505) * [DOCS] Adds ML API examples (DELETE ops) (#2518) * [DOCS] Adds anomaly detection API examples. * [DOCS] Adds more ML examples. * [DOCS] Further edits. * [DOCS] Fixes style. * [DOCS] Adds example requests and responses to ML APIs (C-E) (#2521) Co-authored-by: Quentin Pradet <[email protected]> --------- Co-authored-by: Lisa Cawley <[email protected]> Co-authored-by: István Zoltán Szabó <[email protected]>
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‎specification/ml/clear_trained_model_deployment_cache/examples/200_response/MlClearTrainedModelDeploymentCacheResponseExample1.json

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{
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"summary": "A successful response when clearing the inference cache.",
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"value": {
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"cleared": true
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}
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}

‎specification/ml/close_job/examples/200_response/MlCloseJobResponseExample1.json

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{
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"summary": "A successful response when closing anomaly detection jobs.",
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"value": {
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"closed": true
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}
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}

‎specification/ml/delete_calendar/examples/200_response/MlDeleteCalendarResponseExample1.json

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{
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"summary": "A successful response when deleting a calendar.",
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"value": {
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"acknowledged": true
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}
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}

‎specification/ml/delete_calendar_event/examples/200_response/MlDeleteCalendarEventResponseExample1.json

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{
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"summary": "A successful response when deleting a calendar event.",
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"value": {
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"acknowledged": true
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}
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}

‎specification/ml/delete_calendar_job/examples/200_response/MlDeleteCalendarJobResponseExample1.json

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{
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"summary": "A successful response when deleting an anomaly detection job from a calendar.",
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"value": {
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"calendar_id": "planned-outages",
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"job_ids": []
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}
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}

‎specification/ml/delete_data_frame_analytics/examples/200_response/MlDeleteDataFrameAnalyticsResponseExample1.json

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{
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"summary": "A successful response when deleting a data frame analytics job.",
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"value": {
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"acknowledged": true
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}
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}

‎specification/ml/delete_datafeed/examples/200_response/MlDeleteDatafeedResponseExample1.json

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{
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"summary": "A successful response when deleting a datafeed.",
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"value": {
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"acknowledged": true
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}
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}

‎specification/ml/delete_expired_data/examples/200_response/MlDeleteExpiredDataResponseExample1.json

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{
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"summary": "A successful response when deleting expired and unused anomaly detection data.",
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"value": {
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"deleted": true
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}
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}

‎specification/ml/delete_filter/examples/200_response/MlDeleteFilterResponseExample1.json

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{
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"summary": "A successful response when deleting a filter.",
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"value": {
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"acknowledged": true
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}
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}

‎specification/ml/delete_forecast/examples/200_response/MlDeleteForecastResponseExample1.json

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{
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"summary": "A successful response when deleting a forecast from an anomaly detection job.",
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"value": {
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"acknowledged": true
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}
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}

‎specification/ml/delete_job/examples/200_response/MlDeleteJobResponseExample1.json

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{
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"summary": "A successful response when deleting an anomaly detection job.",
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"value": {
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"acknowledged": true
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}
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}

‎specification/ml/delete_job/examples/200_response/MlDeleteJobResponseExample2.json

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{
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"summary": "A successful response when deleting an anomaly detection job asynchronously.",
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"description": "When the `wait_for_completion` query parameter is set to `false`, the response contains an identifier for the job deletion task.",
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"value": {
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"task": "oTUltX4IQMOUUVeiohTt8A:39"
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}
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}

‎specification/ml/delete_model_snapshot/examples/200_responses/MlDeleteModelSnapshotResponseExample1.json

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{
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"summary": "A successful response when deleting an existing model snapshot.",
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"value": {
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"acknowledged": true
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}
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}

‎specification/ml/delete_trained_model/examples/200_response/MlDeleteTrainedModelResponseExample1.json

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{
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"summary": "A successful response when deleting an existing trained inference model.",
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"value": {
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"acknowledged": true
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}
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}

‎specification/ml/delete_trained_model_alias/examples/MlDeleteTrainedModelAliasResponseExample1.json

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{
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"summary": "A successful response when deleting a trained model alias.",
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"value": {
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"acknowledged": true
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}
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}

‎specification/ml/estimate_model_memory/examples/200_response/MlEstimateModelMemoryResponseExample1.json

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{
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"summary": "A successful response when estimating sufficent memory for a model.",
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"value": {
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"model_memory_estimate": "21mb"
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}
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}

‎specification/ml/estimate_model_memory/examples/request/MlEstimateModelMemoryRequestExample1.json

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{
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"summary": "A request of estimating model memory limit based on the analysis configuration details provided in the body.",
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"value": {
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"analysis_config": {
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"bucket_span": "5m",
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"detectors": [
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{
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"function": "sum",
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"field_name": "bytes",
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"by_field_name": "status",
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"partition_field_name": "app"
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}
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],
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"influencers": ["source_ip", "dest_ip"]
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},
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"overall_cardinality": {
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"status": 10,
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"app": 50
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},
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"max_bucket_cardinality": {
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"source_ip": 300,
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"dest_ip": 30
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}
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}
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}

‎specification/ml/evaluate_data_frame/examples/200_response/MlEvaluateDataFrameResponseClassificationExample1.json

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{
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"summary": "A succesful response for evaluating a classification analysis job for an annotated index.",
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"description": "The `actual_class` contains the name of the class the analysis tried to predict. The `actual_class_doc_count` is the number of documents in the index belonging to the `actual_class`. The `predicted_classes` object contains the list of the predicted classes and the number of predictions associated with the class.",
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"value": {
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"classification": {
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"multiclass_confusion_matrix": {
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"confusion_matrix": [
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{
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"actual_class": "cat",
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"actual_class_doc_count": 12,
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"predicted_classes": [
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{
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"predicted_class": "cat",
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"count": 12
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},
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{
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"predicted_class": "dog",
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"count": 0
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}
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],
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"other_predicted_class_doc_count": 0
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},
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{
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"actual_class": "dog",
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"actual_class_doc_count": 11,
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"predicted_classes": [
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{
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"predicted_class": "dog",
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"count": 7
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},
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{
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"predicted_class": "cat",
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"count": 4
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}
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],
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"other_predicted_class_doc_count": 0
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}
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],
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"other_actual_class_count": 0
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}
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}
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}
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}

‎specification/ml/evaluate_data_frame/examples/200_response/MlEvaluateDataFrameResponseClassificationExample2.json

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{
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"summary": "A succesful response for evaluating a classification analysis job with the AUC ROC metrics for an annotated index.",
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"value": {
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"classification": {
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"auc_roc": {
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"value": 0.8941788639536681
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}
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}
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}
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}

‎specification/ml/evaluate_data_frame/examples/200_response/MlEvaluateDataFrameResponseOutlierExample.json

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{
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"summary": "A successful response when evaluating an outlier detection job.",
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"value": {
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"outlier_detection": {
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"auc_roc": {
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"value": 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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}

‎specification/ml/evaluate_data_frame/examples/request/MlEvaluateDataFrameRequestClassificationExample1.json

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{
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"summary": "A request for evaluating a classification job for an annotated index.",
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"description": "The `actual_field` contains the ground truth for classification. The `predicted_field` contains the predicted value calculated by the classification analysis.",
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"value": {
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"index": "animal_classification",
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"evaluation": {
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"classification": {
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"actual_field": "animal_class",
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"predicted_field": "ml.animal_class_prediction",
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"metrics": {
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"multiclass_confusion_matrix": {}
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}
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}
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}
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}
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}

‎specification/ml/evaluate_data_frame/examples/request/MlEvaluateDataFrameRequestClassificationExample2.json

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{
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"summary": "A request for evaluating a classification job with AUC ROC metrics for an annotated index.",
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"description": "The `class_name` specifies the class name that is treated as positive during the evaluation, all the other classes are treated as negative.",
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"value": {
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"index": "animal_classification",
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"evaluation": {
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"classification": {
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"actual_field": "animal_class",
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"metrics": {
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"auc_roc": {
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"class_name": "dog"
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}
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}
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}
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}
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}
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}

‎specification/ml/evaluate_data_frame/examples/request/MlEvaluateDataFrameRequestOutlierExample.json

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{
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"summary": "A request for evaluating an outlier detection job for an annotated index.",
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"value": {
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"index": "my_analytics_dest_index",
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"evaluation": {
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"outlier_detection": {
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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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}

‎specification/ml/evaluate_data_frame/examples/request/MlEvaluateDataFrameRequestRegressionExample1.json

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{
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"summary": "A request for evaluating the testing error of a regression job for an annotated index.",
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"description": "The term query in the body limits evaluation to be performed on the test split only. The `actual_field` contains the ground truth for house prices. The `predicted_field` contains the house price calculated by the regression analysis.",
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"value": {
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"index": "house_price_predictions",
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"query": {
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"bool": {
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"filter": [
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{
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"term": {
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"ml.is_training": false
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}
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}
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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",
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"predicted_field": "ml.price_prediction",
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"metrics": {
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"r_squared": {},
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"mse": {},
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"msle": {
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"offset": 10
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},
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"huber": {
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"delta": 1.5
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}
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}
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}
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}
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}
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}

‎specification/ml/evaluate_data_frame/examples/request/MlEvaluateDataFrameRequestRegressionExample2.json

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{
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"summary": "A request for evaluating the training error of a regression job for an annotated index.",
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"description": "The term query in the body limits evaluation to be performed on the training split only. The `actual_field` contains the ground truth for house prices. The `predicted_field` contains the house price calculated by the regression analysis.",
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"value": {
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"index": "house_price_predictions",
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"query": {
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"term": {
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"ml.is_training": {
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"value": true
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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",
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"predicted_field": "ml.price_prediction",
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"metrics": {
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"r_squared": {},
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"mse": {},
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"msle": {},
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"huber": {}
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}
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}
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}
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}
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}

‎specification/ml/explain_data_frame_analytics/examples/200_response/MlExplainDataFrameAnalyticsResponseExample1.json

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{
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"summary": "A succesful response for explaining a data frame analytics job configuration.",
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"value": {
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"field_selection": [
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{
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"field": "number_of_bedrooms",
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"mappings_types": ["integer"],
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"is_included": true,
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"is_required": false,
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"feature_type": "numerical"
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},
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{
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"field": "postcode",
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"mappings_types": ["text"],
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"is_included": false,
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"is_required": false,
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"reason": "[postcode.keyword] is preferred because it is aggregatable"
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},
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{
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"field": "postcode.keyword",
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"mappings_types": ["keyword"],
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"is_included": true,
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"is_required": false,
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"feature_type": "categorical"
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},
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{
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"field": "price",
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"mappings_types": ["float"],
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"is_included": true,
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"is_required": true,
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"feature_type": "numerical"
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}
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],
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"memory_estimation": {
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"expected_memory_without_disk": "128MB",
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"expected_memory_with_disk": "32MB"
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}
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}
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}

‎specification/ml/explain_data_frame_analytics/examples/request/MlExplainDataFrameAnalyticsRequestExample1.json

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{
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"summary": "A request for explaining a data frame analytics job configuration.",
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"value": {
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"source": {
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"index": "houses_sold_last_10_yrs"
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},
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"analysis": {
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"regression": {
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"dependent_variable": "price"
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}
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}
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}
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}

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