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Table of Contents

Creating models

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In the Model creation step, you get sample models: one default regression model, one default decision tree model, and optionally by a benchmark model or models. During modeling, you can add more models and save them. A good practice is to create each type of model and compare their key characteristics.

  • In the Model creation step, check the following data:

    • To verify the predictive performance achieved by the model based on the development set, check the Development set column.
    • To verify the predictive performance achieved by the model based on the test set, check the Test set column.
    • To verify the predictive performance achieved by the model based on the validation set, check the Validation set column.
    • To verify the number of predictors used in the model, check the # Predictors column.
    • To verify the list of predictors in the model, check the Predictors column.
  • Creating a regression model

    Create a model that works well on linear data.

  • Creating a decision tree model

    Create a model that works well on mid-volume, highly non-linear data.

  • Creating a bivariate model

    Build a model with two predictors that have univariate performance.

  • Creating a genetic algorithm model

    Create a genetic algorithm model while you are building predictive models to generate highly predictive, non-linear models. A genetic algorithm solves optimization problems by creating a generation of possible solutions to the problem.

  • Computation models

    The process of model development allows you to create such default models as regression, decision tree, genetic algorithm, and bivariate.

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