/* * Copyright 2018-2023 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. */ package com.amazonaws.services.sagemaker.model; import java.io.Serializable; import javax.annotation.Generated; import com.amazonaws.protocol.StructuredPojo; import com.amazonaws.protocol.ProtocolMarshaller; /** *

* Specifies a metric to minimize or maximize as the objective of a job. *

* * @see AWS API * Documentation */ @Generated("com.amazonaws:aws-java-sdk-code-generator") public class AutoMLJobObjective implements Serializable, Cloneable, StructuredPojo { /** *

* The name of the objective metric used to measure the predictive quality of a machine learning system. During * training, the model's parameters are updated iteratively to optimize its performance based on the feedback * provided by the objective metric when evaluating the model on the validation dataset. *

* For the list of all available metrics supported by Autopilot, see Autopilot metrics. *

* If you do not specify a metric explicitly, the default behavior is to automatically use: *

*
* For tabular problem types: *
*
- *
  * Regression: MSE. *
  *
- *
  * Binary classification: F1. *
  *
- *
  * Multiclass classification: Accuracy. *
  *
*
*
* For image or text classification problem types: Accuracy *
*
*
* For time-series forecasting problem types: AverageWeightedQuantileLoss *
*

*/ private String metricName; /** *

* For the list of all available metrics supported by Autopilot, see Autopilot metrics. *

* If you do not specify a metric explicitly, the default behavior is to automatically use: *

*
* For tabular problem types: *
*
- *
  * Regression: MSE. *
  *
- *
  * Binary classification: F1. *
  *
- *
  * Multiclass classification: Accuracy. *
  *
*
*
* For image or text classification problem types: Accuracy *
*
*
* For time-series forecasting problem types: AverageWeightedQuantileLoss *
*

* * @param metricName * The name of the objective metric used to measure the predictive quality of a machine learning system. * During training, the model's parameters are updated iteratively to optimize its performance based on the * feedback provided by the objective metric when evaluating the model on the validation dataset.

* For the list of all available metrics supported by Autopilot, see Autopilot metrics. *

* If you do not specify a metric explicitly, the default behavior is to automatically use: *

*
* For tabular problem types: *
*
- *
  * Regression: MSE. *
  *
- *
  * Binary classification: F1. *
  *
- *
  * Multiclass classification: Accuracy. *
  *
*
*
* For image or text classification problem types: Accuracy *
*
*
* For time-series forecasting problem types: AverageWeightedQuantileLoss *
*

* For the list of all available metrics supported by Autopilot, see Autopilot metrics. *

* If you do not specify a metric explicitly, the default behavior is to automatically use: *

*
* For tabular problem types: *
*
- *
  * Regression: MSE. *
  *
- *
  * Binary classification: F1. *
  *
- *
  * Multiclass classification: Accuracy. *
  *
*
*
* For image or text classification problem types: Accuracy *
*
*
* For time-series forecasting problem types: AverageWeightedQuantileLoss *
*

* For the list of all available metrics supported by Autopilot, see Autopilot metrics. *

* If you do not specify a metric explicitly, the default behavior is to automatically use: *

*
* For tabular problem types: *
*
- *
  * Regression: MSE. *
  *
- *
  * Binary classification: F1. *
  *
- *
  * Multiclass classification: Accuracy. *
  *
*
*
* For image or text classification problem types: Accuracy *
*
*
* For time-series forecasting problem types: AverageWeightedQuantileLoss *
*

* For the list of all available metrics supported by Autopilot, see Autopilot metrics. *

* If you do not specify a metric explicitly, the default behavior is to automatically use: *

*
* For tabular problem types: *
*
- *
  * Regression: MSE. *
  *
- *
  * Binary classification: F1. *
  *
- *
  * Multiclass classification: Accuracy. *
  *
*
*
* For image or text classification problem types: Accuracy *
*
*
* For time-series forecasting problem types: AverageWeightedQuantileLoss *
*

* For the list of all available metrics supported by Autopilot, see Autopilot metrics. *

* If you do not specify a metric explicitly, the default behavior is to automatically use: *

*
* For tabular problem types: *
*
- *
  * Regression: MSE. *
  *
- *
  * Binary classification: F1. *
  *
- *
  * Multiclass classification: Accuracy. *
  *
*
*
* For image or text classification problem types: Accuracy *
*
*
* For time-series forecasting problem types: AverageWeightedQuantileLoss *
*

* For the list of all available metrics supported by Autopilot, see Autopilot metrics. *

* If you do not specify a metric explicitly, the default behavior is to automatically use: *

*
* For tabular problem types: *
*
- *
  * Regression: MSE. *
  *
- *
  * Binary classification: F1. *
  *
- *
  * Multiclass classification: Accuracy. *
  *
*
*
* For image or text classification problem types: Accuracy *
*
*
* For time-series forecasting problem types: AverageWeightedQuantileLoss *
*

* For the list of all available metrics supported by Autopilot, see Autopilot metrics. *

* If you do not specify a metric explicitly, the default behavior is to automatically use: *

*
* For tabular problem types: *
*
- *
  * Regression: MSE. *
  *
- *
  * Binary classification: F1. *
  *
- *
  * Multiclass classification: Accuracy. *
  *
*
*
* For image or text classification problem types: Accuracy *
*
*
* For time-series forecasting problem types: AverageWeightedQuantileLoss *
*