IJSRP, Volume 13, Issue 10, October 2023 Edition [ISSN 2250-3153]
The widespread adoption of Machine Learning (ML) across industries has facilitated the use of data-driven decision-making and automation. However, concerns regarding the reliability and robustness of ML models persist. To ensure that ML models perform as intended, are unbiased, and generalize well to new data, comprehensive testing is essential. In this paper, Firstly, we elucidate and expound upon the obstacles that necessitate attention when assessing ML programs. Subsequently, we document the extant resolutions discovered in scholarly works pertaining to the assessment of ML programs. Lastly, we discern areas of deﬁciency within the literature concerning the evaluation of ML programs and proﬀer suggestions for prospective avenues of research within the scientiﬁc community.