CCR:正确分类率
“正确分类率”(Correct Classification Rate,常缩写为CCR)是评估分类模型性能的常用指标之一,广泛应用于机器学习、数据挖掘及各类数据分析任务中。该术语的缩写形式CCR便于快速书写和日常沟通,能够有效提高专业交流的效率。在学术研究或工程应用中,CCR常作为衡量模型分类准确性的基础标准之一,其数值高低直接反映了分类算法的有效性和可靠性。
Correct Classification Rate具体释义
Correct Classification Rate的英文发音
例句
- By using the optimized covariance matrices to optimize the new regularized discriminant analysis ( RDA ), the correct classification rate is higher than that by the old RDA.
- 利用优化的协方差矩阵对正则化判别分析方法进行优化,其模式分类正确率有显著提高。
- The classification performance of extracted features was tested by an index called correct classification rate.
- 以分类正确率为指标检验了提取特征的性能。
- Result shows that the method is simple, convenient and practical and has high correct classification rate.
- 结果表明:判别方法简单方便,正确率高,具有较强实用性。
- For training samples their correct classification rate is 100 %.
- 训练样本的分类正确率达100%。
- It was shown by the result of discrimination analysis that the correct classification rate of wheat and weeds detection with the selected wavelength points achieved 97 %.
- 统计分析的结果表明:运用选定的特征波长点建立判别模型识别小麦和杂草的正确识别率达到了97%;
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