Evaluate data frame analytics | Elasticsearch API (v8)

Evaluate data frame analytics Added in 7.3.0

POST /_ml/data_frame/_evaluate

The API packages together commonly used evaluation metrics for various types of machine learning features. This has been designed for use on indexes created by data frame analytics. Evaluation requires both a ground truth field and an analytics result field to be present.

application/json

Body Required

evaluation object Required

Additional properties are allowed.
Hide evaluation attributes Show evaluation attributes object
- classification object
  
  Additional properties are allowed.
  Hide classification attributes Show classification attributes object
  
  actual_field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  predicted_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  top_classes_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  metrics object
  
  Additional properties are allowed.
  
  Hide metrics attributes Show metrics attributes object
  
  auc_roc object
  
  Additional properties are allowed.
  
  Hide auc_roc attributes Show auc_roc attributes object
  
  class_name string
  
  include_curve boolean
  
  Whether or not the curve should be returned in addition to the score. Default value is false.
  
  precision object
  
  Precision of predictions (per-class and average).
  
  Hide precision attribute Show precision attribute object
  
  * object Additional properties
  
  Additional properties are allowed.
  
  recall object
  
  Recall of predictions (per-class and average).
  
  Hide recall attribute Show recall attribute object
  
  * object Additional properties
  
  Additional properties are allowed.
  
  accuracy object
  
  Accuracy of predictions (per-class and overall).
  
  Hide accuracy attribute Show accuracy attribute object
  
  * object Additional properties
  
  Additional properties are allowed.
  
  multiclass_confusion_matrix object
  
  Multiclass confusion matrix.
  
  Hide multiclass_confusion_matrix attribute Show multiclass_confusion_matrix attribute object
  
  * object Additional properties
  
  Additional properties are allowed.
- outlier_detection object
  
  Additional properties are allowed.
  Hide outlier_detection attributes Show outlier_detection attributes object
  
  actual_field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  predicted_probability_field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  metrics object
  
  Additional properties are allowed.
  
  Hide metrics attributes Show metrics attributes object
  
  auc_roc object
  
  Additional properties are allowed.
  
  Hide auc_roc attributes Show auc_roc attributes object
  
  class_name string
  
  include_curve boolean
  
  Whether or not the curve should be returned in addition to the score. Default value is false.
  
  precision object
  
  Precision of predictions (per-class and average).
  
  Hide precision attribute Show precision attribute object
  
  * object Additional properties
  
  Additional properties are allowed.
  
  recall object
  
  Recall of predictions (per-class and average).
  
  Hide recall attribute Show recall attribute object
  
  * object Additional properties
  
  Additional properties are allowed.
  
  confusion_matrix object
  
  Accuracy of predictions (per-class and overall).
  
  Hide confusion_matrix attribute Show confusion_matrix attribute object
  
  * object Additional properties
  
  Additional properties are allowed.
- regression object
  
  Additional properties are allowed.
  Hide regression attributes Show regression attributes object
  
  actual_field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  predicted_field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  metrics object
  
  Additional properties are allowed.
  
  Hide metrics attributes Show metrics attributes object
  
  mse object
  
  Average squared difference between the predicted values and the actual (ground truth) value. For more information, read this wiki article.
  
  Hide mse attribute Show mse attribute object
  
  * object Additional properties
  
  Additional properties are allowed.
  
  msle object
  
  Additional properties are allowed.
  
  Hide msle attribute Show msle attribute object
  
  offset number
  
  Defines the transition point at which you switch from minimizing quadratic error to minimizing quadratic log error. Defaults to 1.
  
  huber object
  
  Additional properties are allowed.
  
  Hide huber attribute Show huber attribute object
  
  delta number
  
  Approximates 1/2 (prediction - actual)2 for values much less than delta and approximates a straight line with slope delta for values much larger than delta. Defaults to 1. Delta needs to be greater than 0.
  
  r_squared object
  
  Proportion of the variance in the dependent variable that is predictable from the independent variables.
  
  Hide r_squared attribute Show r_squared attribute object
  
  * object Additional properties
  
  Additional properties are allowed.
index string Required
query object

Additional properties are allowed.

External documentation

Responses

200 application/json
Hide response attributes Show response attributes object
- classification object
  
  Additional properties are allowed.
  
  Hide classification attributes Show classification attributes object
  
  auc_roc object
  
  Additional properties are allowed.
  
  Hide auc_roc attributes Show auc_roc attributes object
  
  value number Required
  
  curve array[object]
  
  Hide curve attributes Show curve attributes object
  
  tpr number Required
  
  fpr number Required
  
  threshold number Required
  
  accuracy object
  
  Additional properties are allowed.
  
  Hide accuracy attributes Show accuracy attributes object
  
  classes array[object] Required
  
  Hide classes attributes Show classes attributes object
  
  value number Required
  
  class_name string Required
  
  overall_accuracy number Required
  
  multiclass_confusion_matrix object
  
  Additional properties are allowed.
  
  Hide multiclass_confusion_matrix attributes Show multiclass_confusion_matrix attributes object
  
  confusion_matrix array[object] Required
  
  Hide confusion_matrix attributes Show confusion_matrix attributes object
  
  actual_class string Required
  
  actual_class_doc_count number Required
  
  predicted_classes array[object] Required
  
  Additional properties are allowed.
  
  other_predicted_class_doc_count number Required
  
  other_actual_class_count number Required
  
  precision object
  
  Additional properties are allowed.
  
  Hide precision attributes Show precision attributes object
  
  classes array[object] Required
  
  Hide classes attributes Show classes attributes object
  
  value number Required
  
  class_name string Required
  
  avg_precision number Required
  
  recall object
  
  Additional properties are allowed.
  
  Hide recall attributes Show recall attributes object
  
  classes array[object] Required
  
  Hide classes attributes Show classes attributes object
  
  value number Required
  
  class_name string Required
  
  avg_recall number Required
- outlier_detection object
  
  Additional properties are allowed.
  
  Hide outlier_detection attributes Show outlier_detection attributes object
  
  auc_roc object
  
  Additional properties are allowed.
  
  Hide auc_roc attributes Show auc_roc attributes object
  
  value number Required
  
  curve array[object]
  
  Hide curve attributes Show curve attributes object
  
  tpr number Required
  
  fpr number Required
  
  threshold number Required
  
  precision object
  
  Set the different thresholds of the outlier score at where the metric is calculated.
  
  Hide precision attribute Show precision attribute object
  
  * number Additional properties
  
  recall object
  
  Set the different thresholds of the outlier score at where the metric is calculated.
  
  Hide recall attribute Show recall attribute object
  
  * number Additional properties
  
  confusion_matrix object
  
  Set the different thresholds of the outlier score at where the metrics (tp - true positive, fp - false positive, tn - true negative, fn - false negative) are calculated.
  
  Hide confusion_matrix attribute Show confusion_matrix attribute object
  
  * object Additional properties
  
  Additional properties are allowed.
  
  Hide * attributes Show * attributes object
  
  tp number Required
  
  True Positive
  
  fp number Required
  
  False Positive
  
  tn number Required
  
  True Negative
  
  fn number Required
  
  False Negative
- regression object
  
  Additional properties are allowed.
  
  Hide regression attributes Show regression attributes object
  
  huber object
  
  Additional properties are allowed.
  
  Hide huber attribute Show huber attribute object
  
  value number Required
  
  mse object
  
  Additional properties are allowed.
  
  Hide mse attribute Show mse attribute object
  
  value number Required
  
  msle object
  
  Additional properties are allowed.
  
  Hide msle attribute Show msle attribute object
  
  value number Required
  
  r_squared object
  
  Additional properties are allowed.
  
  Hide r_squared attribute Show r_squared attribute object
  
  value number Required

POST /_ml/data_frame/_evaluate

curl \
 -X POST http://api.example.com/_ml/data_frame/_evaluate \
 -H "Content-Type: application/json" \
 -d '{"evaluation":{"classification":{"actual_field":"string","predicted_field":"string","top_classes_field":"string","":{"auc_roc":{"class_name":"string","include_curve":true},"precision":{"additionalProperty1":{},"additionalProperty2":{}},"recall":{"additionalProperty1":{},"additionalProperty2":{}},"accuracy":{"additionalProperty1":{},"additionalProperty2":{}},"multiclass_confusion_matrix":{"additionalProperty1":{},"additionalProperty2":{}}}},"outlier_detection":{"actual_field":"string","predicted_probability_field":"string","":{"auc_roc":{"class_name":"string","include_curve":true},"precision":{"additionalProperty1":{},"additionalProperty2":{}},"recall":{"additionalProperty1":{},"additionalProperty2":{}},"confusion_matrix":{"additionalProperty1":{},"additionalProperty2":{}}}},"regression":{"actual_field":"string","predicted_field":"string","metrics":{"mse":{"additionalProperty1":{},"additionalProperty2":{}},"msle":{"offset":42.0},"huber":{"delta":42.0},"r_squared":{"additionalProperty1":{},"additionalProperty2":{}}}}},"index":"string","query":{}}'

Request examples

{
  "evaluation": {
    "classification": {
      "actual_field": "string",
      "predicted_field": "string",
      "top_classes_field": "string",
      "": {
        "auc_roc": {
          "class_name": "string",
          "include_curve": true
        },
        "precision": {
          "additionalProperty1": {},
          "additionalProperty2": {}
        },
        "recall": {
          "additionalProperty1": {},
          "additionalProperty2": {}
        },
        "accuracy": {
          "additionalProperty1": {},
          "additionalProperty2": {}
        },
        "multiclass_confusion_matrix": {
          "additionalProperty1": {},
          "additionalProperty2": {}
        }
      }
    },
    "outlier_detection": {
      "actual_field": "string",
      "predicted_probability_field": "string",
      "": {
        "auc_roc": {
          "class_name": "string",
          "include_curve": true
        },
        "precision": {
          "additionalProperty1": {},
          "additionalProperty2": {}
        },
        "recall": {
          "additionalProperty1": {},
          "additionalProperty2": {}
        },
        "confusion_matrix": {
          "additionalProperty1": {},
          "additionalProperty2": {}
        }
      }
    },
    "regression": {
      "actual_field": "string",
      "predicted_field": "string",
      "metrics": {
        "mse": {
          "additionalProperty1": {},
          "additionalProperty2": {}
        },
        "msle": {
          "offset": 42.0
        },
        "huber": {
          "delta": 42.0
        },
        "r_squared": {
          "additionalProperty1": {},
          "additionalProperty2": {}
        }
      }
    }
  },
  "index": "string",
  "query": {}
}

Response examples (200)

{
  "classification": {
    "": {
      "value": 42.0,
      "curve": [
        {
          "tpr": 42.0,
          "fpr": 42.0,
          "threshold": 42.0
        }
      ]
    },
    "accuracy": {
      "classes": [
        {
          "value": 42.0,
          "class_name": "string"
        }
      ],
      "overall_accuracy": 42.0
    },
    "multiclass_confusion_matrix": {
      "confusion_matrix": [
        {
          "actual_class": "string",
          "actual_class_doc_count": 42.0,
          "predicted_classes": [
            {}
          ],
          "other_predicted_class_doc_count": 42.0
        }
      ],
      "other_actual_class_count": 42.0
    },
    "precision": {
      "classes": [
        {
          "value": 42.0,
          "class_name": "string"
        }
      ],
      "avg_precision": 42.0
    },
    "recall": {
      "classes": [
        {
          "value": 42.0,
          "class_name": "string"
        }
      ],
      "avg_recall": 42.0
    }
  },
  "outlier_detection": {
    "": {
      "value": 42.0,
      "curve": [
        {
          "tpr": 42.0,
          "fpr": 42.0,
          "threshold": 42.0
        }
      ]
    },
    "precision": {
      "additionalProperty1": 42.0,
      "additionalProperty2": 42.0
    },
    "recall": {
      "additionalProperty1": 42.0,
      "additionalProperty2": 42.0
    },
    "confusion_matrix": {
      "additionalProperty1": {
        "tp": 42.0,
        "fp": 42.0,
        "tn": 42.0,
        "fn": 42.0
      },
      "additionalProperty2": {
        "tp": 42.0,
        "fp": 42.0,
        "tn": 42.0,
        "fn": 42.0
      }
    }
  },
  "regression": {
    "huber": {
      "value": 42.0
    },
    "mse": {
      "value": 42.0
    },
    "msle": {
      "value": 42.0
    },
    "r_squared": {
      "value": 42.0
    }
  }
}