Analysis of Statistic Metrics in Different Types of Machine Learning
DOI:
https://doi.org/10.54097/c4mz2q66Keywords:
Machine learning; evaluation metrics; regression; clustering; classification.Abstract
As a matter of fact, machine leading techniques has been attracting a lot of attention as one of the most promising areas of development in recent years on account of the rapid development of computing ability. Among other things, these autonomous learning mods need to go through a series of evaluations to determine their utility and usability and other features. In addition, this is where statistic metrics become particularly important. With this in mind, this paper will systematically analyze the use of different evaluation metrics and evaluation methods from three aspects, i.e., regression, classification and clustering. To be specific, some examples and formulae will be interspersed with practical examples. The purpose of this paper is to help better understand the different statistic metrics, so that they can apply them to different models of machine learning. Overall, these results shed light on guiding further exploration of machine learning implementation in various aspects.
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