Advancements in Machine Learning Algorithms: Creating a New Era of Professional Predictive Analytics for Increased Effectiveness of Decision Making
Keywords:
Predictive models, decision tree, learning algorithm, over fitted model, interpretation, resources, bias, data privacy, accountability, trained models, decidability, stability, practicality, sample, vend applications, cased-based, future enhancements, equity, reliability, accuracy, best practicesAbstract
Forecasting (predictive) analytics has been empowered by machine learning (ML) as a discipline leading to the development of potent instruments for outcome prediction and decision-making enhancement across many domains, such as healthcare, finance, retailing, manufacturing, and transportation. This paper looks at how and what the new advanced ML algorithms in predictive analytics entail and its important applications, and case studies that show its relevance in business areas and domains. Nevertheless, the broader adoption of ML in risk analytics has its challenges and restriction. These are data issues, model issues, model’s interpretability and over fitting, and lastly the issues to do with computational resource needed for model’s development and implementation. Other issues for concern include bias, discrimination, and privacy matters all of which must be taken into account to the enable the proper implementation of the ML technologies. However, model maintenance and scalability are issues because no one model stays great or optimal indefinitely. In conclusion, the article asserts that despite the fact that ML uncovers great possibilities of optimum use to foster efficiency gains, overcoming these issues needs more than a fix; it entails the improved way of data management the ML explain ability initiatives, the ethical regulation of governance and constant model refinement. It is now crucial to overcome these barriers while providing organizations with justified, transparent, and effective use of ML for predictive analytics.
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