Big data firms and their customers are increasingly using machine learning techniques to improve analytics and predictive analytic systems that analyse, predict, and predict, on vast quantities of data. When using machine-learning techniques, it is important to identify the root causes of error, which can be analyzed by researchers and machine learning models. However, most researchers have very few time to focus on them and often they don't have research or technical expertise to analyse the roots of error.
In this article, we would like to extend and improve the "straw man" problem. It is useful for researchers and evaluators to evaluate machine learning algorithms because they may have been used too infrequently.
The original straw man problem comes from the book "Machine Learning Approaches and Theory" by the late C. H. Fisher. It was introduced by H. A. Parker in 1967 and asked to explain why certain results were the result of machine-generated hypotheses. The original problem is now considered a vignette, and there are only several solutions in literature.
References:
In addition to these problems, a few problems and reviews of those problems are listed at http://citeseerxml.com/cite/rca17b/c358
The paper "Review of some of the most important aspects of online pulse manipulation algorithms: BGAN and SVD" is on the same page as the topic.