Format ML examples

Author: lcawl

specification/ml/estimate_model_memory/MlEstimateModelMemoryRequestExample1.json

@@ -0,0 +1,21 @@
+# summary:
+# method_request: POST _ml/anomaly_detectors/_estimate_model_memory
+description: Run `POST _ml/anomaly_detectors/_estimate_model_memory` to estimate the model memory limit based on the analysis configuration details provided in the request body.
+# type: request
+value:
+  analysis_config:
+    bucket_span: 5m
+    detectors:
+      - function: sum
+        field_name: bytes
+        by_field_name: status
+        partition_field_name: app
+    influencers:
+      - source_ip
+      - dest_ip
+  overall_cardinality:
+    status: 10
+    app: 50
+  max_bucket_cardinality:
+    source_ip: 300
+    dest_ip: 30

specification/ml/estimate_model_memory/MlEstimateModelMemoryResponseExample1.json

@@ -0,0 +1,6 @@
+# summary:
+description: A successful response from `POST _ml/anomaly_detectors/_estimate_model_memory`.
+type: response
+response_code: 200
+value:
+  model_memory_estimate: 21mb

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

@@ -26,8 +26,19 @@ import {
 /** @variants container */
 export class Response {
   body: {
+    /**
+     * Evaluation results for a classification analysis.
+     * It outputs a prediction that identifies to which of the classes each document belongs.
+     */
     classification?: DataframeClassificationSummary
+    /**
+     * Evaluation results for an outlier detection analysis.
+     * It outputs the probability that each document is an outlier.
+     */
     outlier_detection?: DataframeOutlierDetectionSummary
+    /**
+     * Evaluation results for a regression analysis which outputs a prediction of values.
+     */
     regression?: DataframeRegressionSummary
   }
 }