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Developing models

Updated on July 5, 2022

The Model development step helps you create models for further analysis. You group predictors based on their behavior and create models to compare their key characteristics.

You can inspect a model in the form of coefficients of the regression formula, as a scorecard, and view model sensitivity. The formula is a model layout that shows the coefficient and statistics for the following predictors: standard error, wald statistic, and significance.
  • Grouping predictors

    Group predictors in the Model development step to prepare reliable models. The process of model development has three default models: regression, decision tree, and bivariate. A common setting that applies to all types of models is the selection of the predictors.

  • Creating models

    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.

  • Benchmark models

    A benchmark model appears unavailable in the Model creation step when you define a benchmark role for a field during the Analyzing data step.

  • Sensitivity of models

    Model sensitivity is the correlation between the behavior predicted by the predictive model and the behavior predicted by one of its predictors.

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