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Developing models

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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.

  • 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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