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.

翻译：我们重新提出回归和分类,目的是验证机器学习算法的结果; 我们的重组简化了原始问题,并验证了利用培训数据进行的机器学习算法的结果; 由于机器学习算法的验证必须始终可以解释,我们用KNN算法以及基于有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,使用了三个公开的数据集,对三个分类和两个回归问题进行了评估。基于有条件概率的算法也是在线的,只需要与KNN算法相比的记忆的一小部分。