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Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining by Glenn J. Myatt

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7.7 SUMMARY

Types of models:

  • Classification: Models where the response variable is categorical. These are assessed using: concordance, error rate, specificity, and sensitivity analysis.

    Table 7.20. Summary of predictive modeling approaches in this chapter

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  • Regression: Models where the response variable is continuous. These are assessed using r2 and residual analysis.

Building a prediction model involves the following steps:

  1. Select methods based on problem
  2. Separate out training and test sets
  3. Optimize the models
  4. Assess models generated

Applying a prediction model follows these steps:

  1. Evaluate whether an observation can be used with the model
  2. Present observations to model
  3. Combine results from multiple models (if appropriate)
  4. Understand confidence and/or explain how results were computed

Table 7.20 summarizes the different methods described in this chapter.

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