Effectiveness and Accuracy of Analytical Models
Watch this video detailing how data science models can be applied in business intelligence. A data science model is a computational framework or mathematical representation designed to analyze and extract insights from data. These models are constructed using various statistics, machine learning, and artificial intelligence techniques to identify patterns, make predictions, or optimize processes within datasets. Data science models can range from simple linear regression models to complex deep learning architectures, depending on the complexity of the problem being addressed and the nature of the data available. They are employed across diverse domains such as finance, healthcare, marketing, and manufacturing to support decision-making, drive innovation, and enhance understanding of complex phenomena. The effectiveness and accuracy of models in business intelligence can be determined through a comprehensive evaluation process encompassing multiple facets. Initially, the model's performance metrics, such as precision and recall, are indicators of its accuracy in predicting outcomes. Assessing the model's ability to generalize to unseen data through techniques like cross-validation provides insights into its robustness. Beyond numerical metrics, stakeholder feedback and domain experts' assessment contribute to understanding the model's relevance and utility within the business context. Regular monitoring and recalibration of the model based on evolving data and business needs to ensure its continued effectiveness over time.
Source: DATAVERSITY, https://www.youtube.com/watch?v=EYBIli9ZaEA
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