Basic concepts

Basic concepts

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a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables).

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Fitting data too closely or exactly to a particular dataset, and may therefore fail to fit unseen data

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the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set

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Analyze data used for classification and regression analysis

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a flowchart-like structure in which each internal node represents a "test" on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label, the paths from root to leaf represent classification rules.

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Supervised Learning