.. ****************************************************************************** .. * Copyright 2020-2021 Intel Corporation .. * .. * Licensed under the Apache License, Version 2.0 (the "License"); .. * you may not use this file except in compliance with the License. .. * You may obtain a copy of the License at .. * .. * http://www.apache.org/licenses/LICENSE-2.0 .. * .. * Unless required by applicable law or agreed to in writing, software .. * distributed under the License 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. .. *******************************************************************************/ Quality Metrics =============== In |short_name|, a quality metric is a numerical characteristic or a set of connected numerical characteristics that represents the qualitative aspect of the result returned by an algorithm: a computed statistical estimate, model, or result of decision making. A common set of quality metrics can be defined for some training and prediction algorithms. A typical workflow with quality metric set is the following: #. Create a quality metric set object to compute quality metrics. - Set specific parameters for the algorithms. - Use the ``useDefaultMetrics`` flag to specify whether the default or user-defined quality metrics should be computed. #. Get an input collection object using ``QualityMetricsId`` of a specific algorithm. #. Set data to the input collection using the algorithm's ``InputId``. #. Perform computation. #. Get the resulting collection of quality metrics using the algorithm's ``ResultId``. .. note:: For values of ``InputId``, ``Parameters``, ``QualityMetricsId``, ``ResultId``, refer to the description of a specific algorithm. Quality metrics are optional. They are computed when the computation is explicitly requested. .. toctree:: :maxdepth: 2 default-metric-set.rst user-defined.rst