Evaluation ¶

class Evaluation(TypedDict, closed=True):
    evaluation_id: NotRequired["capo_machine_learning.types.entity_id.EntityId"]
    """<p>The ID that is assigned to the <code>Evaluation</code> at creation.</p>"""
    ml_model_id: NotRequired["capo_machine_learning.types.entity_id.EntityId"]
    """<p>The ID of the <code>MLModel</code> that is the focus of the evaluation.</p>"""
    evaluation_data_source_id: NotRequired[
        "capo_machine_learning.types.entity_id.EntityId"
    ]
    """<p>The ID of the <code>DataSource</code> that is used to evaluate the <code>MLModel</code>.</p>"""
    input_data_location_s3: NotRequired["capo_machine_learning.types.s3_url.S3Url"]
    """<p>The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.</p>"""
    created_by_iam_user: NotRequired[
        "capo_machine_learning.types.aws_user_arn.AwsUserArn"
    ]
    """<p>The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.</p>"""
    created_at: NotRequired["capo_machine_learning.types.epoch_time.EpochTime"]
    """<p>The time that the <code>Evaluation</code> was created. The time is expressed in epoch time.</p>"""
    last_updated_at: NotRequired["capo_machine_learning.types.epoch_time.EpochTime"]
    """<p>The time of the most recent edit to the <code>Evaluation</code>. The time is expressed in epoch time.</p>"""
    name: NotRequired["capo_machine_learning.types.entity_name.EntityName"]
    """<p>A user-supplied name or description of the <code>Evaluation</code>. </p>"""
    status: NotRequired["capo_machine_learning.types.entity_status.EntityStatus"]
    """<p>The status of the evaluation. This element can have one of the following values:</p> <ul> <li> <p> <code>PENDING</code> - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an <code>MLModel</code>.</p> </li> <li> <p> <code>INPROGRESS</code> - The evaluation is underway.</p> </li> <li> <p> <code>FAILED</code> - The request to evaluate an <code>MLModel</code> did not run to completion. It is not usable.</p> </li> <li> <p> <code>COMPLETED</code> - The evaluation process completed successfully.</p> </li> <li> <p> <code>DELETED</code> - The <code>Evaluation</code> is marked as deleted. It is not usable.</p> </li> </ul>"""
    performance_metrics: NotRequired[
        "capo_machine_learning.types.performance_metrics.PerformanceMetrics"
    ]
    """<p>Measurements of how well the <code>MLModel</code> performed, using observations referenced by the <code>DataSource</code>. One of the following metrics is returned, based on the type of the <code>MLModel</code>: </p> <ul> <li> <p>BinaryAUC: A binary <code>MLModel</code> uses the Area Under the Curve (AUC) technique to measure performance. </p> </li> <li> <p>RegressionRMSE: A regression <code>MLModel</code> uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.</p> </li> <li> <p>MulticlassAvgFScore: A multiclass <code>MLModel</code> uses the F1 score technique to measure performance. </p> </li> </ul> <p> For more information about performance metrics, please see the <a href="https://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine Learning Developer Guide</a>. </p>"""
    message: NotRequired["capo_machine_learning.types.message.Message"]
    """<p>A description of the most recent details about evaluating the <code>MLModel</code>.</p>"""
    compute_time: NotRequired["capo_machine_learning.types.long_type.LongType"]
    finished_at: NotRequired["capo_machine_learning.types.epoch_time.EpochTime"]
    started_at: NotRequired["capo_machine_learning.types.epoch_time.EpochTime"]