Practical applications of statistical methods usually involve assumptions about the domain, such as independence, normality, linearity, and the choice of variables. I will describe a set of graphical tools which simplify the process of learning about a domain and checking model assumptions. Some of these tools have been around for years, but vastly underused by statistical learning researchers. Using real-world examples, I will illustrate how visualization can be used for model selection, identifying exceptional cases, and interpreting the results of learning algorithms.<\/p>\n","protected":false},"excerpt":{"rendered":"
Practical applications of statistical methods usually involve assumptions about the domain, such as independence, normality, linearity, and the choice of variables. I will describe a set of graphical tools which simplify the process of learning about a domain and checking model assumptions. Some of these tools have been around for years, but vastly underused by 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