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RELATING STATISTICAL METHODS TO MACHINE LEARNING PREDICTIVE MODELS

机译:将统计方法与机器学习预测模型相关联

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The paper reviewed the probabilistic feature of binomial distribution in the operation of machine learning (ML) classifications. It also examined a normal distribution and the concepts for approximating the binomial distribution to a normal distribution in estimating generalization error and its role in machine learning model selection. Again, it studied the confident interval and hypothesis testing and their estimations in the evaluation and comparison of the Performance metrics (Accuracy) of the learning algorithms. The paper highlighted their statistical significance to the ML models and classifiers as well as the differences in their utilization in statistics and machine learning.
机译:本文回顾了概率的特点二项分布在机器的操作学习(ML)分类。正态分布的概念近似的二项分布正态分布估计泛化在机器学习模型误差及其作用选择。时间间隔和假设检验及其估计的评价和比较的性能指标(精度)学习算法。毫升模型和统计学意义分类器以及他们的差异利用统计和机器学习。

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