论文标题
可解释用于实际应用机器学习模型的在线验证
Explainable Online Validation of Machine Learning Models for Practical Applications
论文作者
论文摘要
我们介绍了回归和分类的重新制定，旨在验证机器学习算法的结果。我们的重新印象简化了原始问题，并使用培训数据验证了机器学习算法的结果。由于必须始终可以解释机器学习算法的验证，因此我们使用KNN算法以及基于条件概率的算法进行实验，这是在这项工作中提出的。为了评估我们的方法，使用了三个公开可用的数据集，并评估了三个分类和两个回归问题。基于条件概率的算法也具有在线功能，与KNN算法相比，仅需要一小部分内存。
We present a reformulation of the regression and classification, which aims to validate the result of a machine learning algorithm. Our reformulation simplifies the original problem and validates the result of the machine learning algorithm using the training data. Since the validation of machine learning algorithms must always be explainable, we perform our experiments with the kNN algorithm as well as with an algorithm based on conditional probabilities, which is proposed in this work. For the evaluation of our approach, three publicly available data sets were used and three classification and two regression problems were evaluated. The presented algorithm based on conditional probabilities is also online capable and requires only a fraction of memory compared to the kNN algorithm.
