[ aws . frauddetector ]

describe-model-versions

Description

Gets all of the model versions for the specified model type or for the specified model type and model ID. You can also get details for a single, specified model version.

See also: AWS API Documentation

See ‘aws help’ for descriptions of global parameters.

Synopsis

  describe-model-versions
[--model-id <value>]
[--model-version-number <value>]
[--model-type <value>]
[--next-token <value>]
[--max-results <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--cli-auto-prompt <value>]

Options

--model-id (string)

The model ID.

--model-version-number (string)

The model version number.

--model-type (string)

The model type.

Possible values:

• ONLINE_FRAUD_INSIGHTS

--next-token (string)

The next token from the previous results.

--max-results (integer)

The maximum number of results to return.

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

--cli-auto-prompt (boolean) Automatically prompt for CLI input parameters.

See ‘aws help’ for descriptions of global parameters.

Output

modelVersionDetails -> (list)

The model version details.

(structure)

The details of the model version.

modelId -> (string)

The model ID.

modelType -> (string)

The model type.

modelVersionNumber -> (string)

The model version number.

status -> (string)

The status of the model version.

trainingDataSource -> (string)

The model version training data source.

trainingDataSchema -> (structure)

The training data schema.

modelVariables -> (list)

The training data schema variables.

(string)

labelSchema -> (structure)

The label schema.

labelMapper -> (map)

The label mapper maps the Amazon Fraud Detector supported model classification labels (FRAUD , LEGIT ) to the appropriate event type labels. For example, if “FRAUD ” and “LEGIT ” are Amazon Fraud Detector supported labels, this mapper could be: {"FRAUD" => ["0"] , "LEGIT" => ["1"]} or {"FRAUD" => ["false"] , "LEGIT" => ["true"]} or {"FRAUD" => ["fraud", "abuse"] , "LEGIT" => ["legit", "safe"]} . The value part of the mapper is a list, because you may have multiple label variants from your event type for a single Amazon Fraud Detector label.

key -> (string)

value -> (list)

(string)

externalEventsDetail -> (structure)

The event details.

dataLocation -> (string)

The Amazon S3 bucket location for the data.

dataAccessRoleArn -> (string)

The ARN of the role that provides Amazon Fraud Detector access to the data location.

trainingResult -> (structure)

The training results.

dataValidationMetrics -> (structure)

The validation metrics.

fileLevelMessages -> (list)

The file-specific model training validation messages.

(structure)

The message details.

title -> (string)

The message title.

content -> (string)

The message content.

type -> (string)

The message type.

fieldLevelMessages -> (list)

The field-specific model training validation messages.

(structure)

The message details.

fieldName -> (string)

The field name.

identifier -> (string)

The message ID.

title -> (string)

The message title.

content -> (string)

The message content.

type -> (string)

The message type.

trainingMetrics -> (structure)

The training metric details.

auc -> (float)

The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has a score of 1.0.

metricDataPoints -> (list)

The data points details.

(structure)

Model performance metrics data points.

fpr -> (float)

The false positive rate. This is the percentage of total legitimate events that are incorrectly predicted as fraud.

precision -> (float)

The percentage of fraud events correctly predicted as fraudulent as compared to all events predicted as fraudulent.

tpr -> (float)

The true positive rate. This is the percentage of total fraud the model detects. Also known as capture rate.

threshold -> (float)

The model threshold that specifies an acceptable fraud capture rate. For example, a threshold of 500 means any model score 500 or above is labeled as fraud.

lastUpdatedTime -> (string)

The timestamp when the model was last updated.

createdTime -> (string)

The timestamp when the model was created.

arn -> (string)

The model version ARN.

nextToken -> (string)

The next token.