Machine Learning for People
Machine Learning
Model Selection
How can I create a model that is easy to use and understand?
Abstract
Through a practical example, we explore the strategic art of selecting machine learning models tailored to specific situational needs, shifting the focus beyond pure accuracy. We take into account both computational expense and ease of understanding when sharing models with others to build a balanced approach to model selection that combines both functional effectiveness and interpretability.
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