Evaluate data frame analytics APIedit
Evaluates the data frame analytics for an annotated index.
This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.
Requestedit
POST _ml/data_frame/_evaluate
Prerequisitesedit
-
You must have
monitor_mlprivilege to use this API. For more information, see Security privileges and Built-in roles.
Descriptionedit
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.
Request bodyedit
-
index -
(Required, object) Defines the
indexin which the evaluation will be performed. -
query - (Optional, object) A query clause that retrieves a subset of data from the source index. See Query DSL.
-
evaluation -
(Required, object) Defines the type of evaluation you want to perform. See Data frame analytics evaluation resources.
Available evaluation types:
-
binary_soft_classification -
regression
-
Examplesedit
Binary soft classificationedit
POST _ml/data_frame/_evaluate
{
"index": "my_analytics_dest_index",
"evaluation": {
"binary_soft_classification": {
"actual_field": "is_outlier",
"predicted_probability_field": "ml.outlier_score"
}
}
}
The API returns the following results:
{
"binary_soft_classification": {
"auc_roc": {
"score": 0.92584757746414444
},
"confusion_matrix": {
"0.25": {
"tp": 5,
"fp": 9,
"tn": 204,
"fn": 5
},
"0.5": {
"tp": 1,
"fp": 5,
"tn": 208,
"fn": 9
},
"0.75": {
"tp": 0,
"fp": 4,
"tn": 209,
"fn": 10
}
},
"precision": {
"0.25": 0.35714285714285715,
"0.5": 0.16666666666666666,
"0.75": 0
},
"recall": {
"0.25": 0.5,
"0.5": 0.1,
"0.75": 0
}
}
}
Regressionedit
POST _ml/data_frame/_evaluate
{
"index": "house_price_predictions",
"query": {
"bool": {
"filter": [
{ "term": { "ml.is_training": false } }
]
}
},
"evaluation": {
"regression": {
"actual_field": "price",
"predicted_field": "ml.price_prediction",
"metrics": {
"r_squared": {},
"mean_squared_error": {}
}
}
}
}
|
The output destination index from a data frame analytics regression analysis. |
|
|
In this example, a test/train split ( |
|
|
The ground truth value for the actual house price. This is required in order to evaluate results. |
|
|
The predicted value for house price calculated by the regression analysis. |
The following example calculates the training error:
POST _ml/data_frame/_evaluate
{
"index": "student_performance_mathematics_reg",
"query": {
"term": {
"ml.is_training": {
"value": true
}
}
},
"evaluation": {
"regression": {
"actual_field": "G3",
"predicted_field": "ml.G3_prediction",
"metrics": {
"r_squared": {},
"mean_squared_error": {}
}
}
}
}
|
In this example, a test/train split ( |
|
|
The field that contains the ground truth value for the actual student performance. This is required in order to evaluate results. |
|
|
The field that contains the predicted value for student performance calculated by the regression analysis. |
The next example calculates the testing error. The only difference compared with
the previous example is that ml.is_training is set to false this time, so
the query excludes the train split from the evaluation.
POST _ml/data_frame/_evaluate
{
"index": "student_performance_mathematics_reg",
"query": {
"term": {
"ml.is_training": {
"value": false
}
}
},
"evaluation": {
"regression": {
"actual_field": "G3",
"predicted_field": "ml.G3_prediction",
"metrics": {
"r_squared": {},
"mean_squared_error": {}
}
}
}
}
|
In this example, a test/train split ( |
|
|
The field that contains the ground truth value for the actual student performance. This is required in order to evaluate results. |
|
|
The field that contains the predicted value for student performance calculated by the regression analysis. |