/* * Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. * * Licensed under the Apache License, Version 2.0 (the "License"). * You may not use this file except in compliance with the License. * A copy of the License is located at * * http://aws.amazon.com/apache2.0 * * or in the "license" file accompanying this file. This file is distributed * on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either * express or implied. See the License for the specific language governing * permissions and limitations under the License. */ /* * Do not modify this file. This file is generated from the frauddetector-2019-11-15.normal.json service model. */ using System; using System.Collections.Generic; using System.Xml.Serialization; using System.Text; using System.IO; using System.Net; using Amazon.Runtime; using Amazon.Runtime.Internal; namespace Amazon.FraudDetector.Model { /// /// The label schema. /// public partial class LabelSchema { private Dictionary> _labelMapper = new Dictionary>(); private UnlabeledEventsTreatment _unlabeledEventsTreatment; /// /// Gets and sets the property LabelMapper. /// /// 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. /// /// public Dictionary> LabelMapper { get { return this._labelMapper; } set { this._labelMapper = value; } } // Check to see if LabelMapper property is set internal bool IsSetLabelMapper() { return this._labelMapper != null && this._labelMapper.Count > 0; } /// /// Gets and sets the property UnlabeledEventsTreatment. /// /// The action to take for unlabeled events. /// ///
  • /// /// Use IGNORE if you want the unlabeled events to be ignored. This is recommended /// when the majority of the events in the dataset are labeled. /// ///
  • /// /// Use FRAUD if you want to categorize all unlabeled events as “Fraud”. /// This is recommended when most of the events in your dataset are fraudulent. /// ///
  • /// /// Use LEGIT if you want to categorize all unlabeled events as “Legit”. /// This is recommended when most of the events in your dataset are legitimate. /// ///
  • /// /// Use AUTO if you want Amazon Fraud Detector to decide how to use the unlabeled /// data. This is recommended when there is significant unlabeled events in the dataset. /// ///
/// /// By default, Amazon Fraud Detector ignores the unlabeled data. /// ///
public UnlabeledEventsTreatment UnlabeledEventsTreatment { get { return this._unlabeledEventsTreatment; } set { this._unlabeledEventsTreatment = value; } } // Check to see if UnlabeledEventsTreatment property is set internal bool IsSetUnlabeledEventsTreatment() { return this._unlabeledEventsTreatment != null; } } }