Bias vs. Variance Tradeoff
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Imagine you are asked to build a machine learning model to decide new loan approvals for a financial firm. You ask the data department in the company for a subset of data to get started working on the problem. The data includes different features about applicants such as age, occupation, zip code, height, number of children, favorite color, etc. You decide to build multiple machine learning models to test out different ideas before settling on the best one.
How would you explain the bias-variance tradeoff with regards to building and choosing a model to use?
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