Predictive analytics and machine learning appear to be used interchangeably in my experience. What are the distinctions among these three terms?
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Machine learning (ML) models can be predictive, prescriptive, or descriptive. ML is a broader term compared to predictive or prescriptive analytics. The distinction between predictive and prescriptive analytics is that prescriptive goes beyond predictive by explaining not only what will happen next but also why it will happen.
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Predictive analytics involves estimating the value of an unknown variable based on the values of known independent variables. For instance, it might involve forecasting future traffic using current and historical traffic trends. Prescriptive analytics focuses on determining the best values for one or more decision variables to optimize one or more metrics while adhering to certain constraints. For example, it might involve choosing the quickest route to a destination by considering predicted traffic conditions. Machine learning is concerned with discovering the function or relationship among a set of variables and is frequently applied in predictive analytics, and occasionally in prescriptive analytics.
I’ve encountered the nuances between predictive analytics, machine learning, and data science. Predictive analytics focuses on using historical data to make forecasts about future events, often employing statistical techniques. Machine learning, a subset of artificial intelligence, involves algorithms that learn from data to improve their performance over time without being explicitly programmed. Data science, encompassing both predictive analytics and machine learning, involves a broader scope of data manipulation, analysis, and interpretation to extract insights and inform decisions across various domains.