The Output tab in the Model Build Settings dialog provides additional options for both controlling the model building process and determining how results are stored and reported.
- Prediction Format - Use this option to set the format in which predictions from the model are output. The options available are different depending upon the model type.
- Create Final Models - This drop-down determines whether LityxIQ will build final models for all iterations or just the single "best" iteration.
- Automatically Build Just the Best Model - Use this option to have LityxIQ build and report a final model for a single iteration - the one that performs the best according to the primary performance metric (see below). All model iterations will still be validated and have performance metrics reported for comparison. This option determines outcome based solely upon quantitative criteria, identifying final coefficients and other output that is unnecessary.
- Build All Models for Review - With this option set, LityxIQ will not only compute performance metrics for all iterations of the model, but will also build a final model for each iteration. Both quantitative performance metrics (such as lift and error rates) and qualitative criteria (such as model coefficients and structure) can be reviewed, prior to deciding upon an iteration to implement. This option will require the longest run times, but it also gives the user complete control of model implementation.
- None: Performance Metrics Only - Use this option to choose for LityxIQ to build no final models. With this option selected, LityxIQ will only compute comparative performance metrics. This option will require the shortest run times, but it will create no final implementable model.
- Primary Performance Metric - Select the performance metric that will be used to compare and rank the order of different model iterations. Relevant options for the selected model type will appear. The main use of this option is when Automatically Build the Best Model is selected above. The "Best" model will be decided based on the metric selected here. It is also used in the case that the model's production version (see https://support.lityxiq.com/396887-Approving-and-Implementing-a-Model) is set to Most Recent and Best
- Prediction Lower/Upper Bounds – Checkboxes and bound setting are used to bound the model with lower and/or upper bounds. The bound setting input boxes will only be available if the relevant checkbox is checked.
- Set Population Average for Target - Check this box if the modeling dataset has a different target variable average than the full population. This is useful with stratified sampling, to construct the modeling dataset. After checking this box, enter the true population average in the data entry box that becomes available. Even if the population average is different than the modeling dataset, it is not necessary to use these settings. The effect will be that predictions resulting from the model will be scaled so that they align more closely to the population average, rather than the modeling dataset average.
- Export Variable Importance and Variable Importance Filename - Checking the Export Variable Importance box will export a csv file with the variable importance scores for each iteration into the File Manager. In the Filename box, enter a template for the filename to be created. Leave it blank for a default filename to be used. Or, you can manually enter the filename, and use the templates [n] for the model name, [v] for the version, and [s] for subversion, or use configuration variables.
- Export Detailed Data, Detailed Data Filename, and Record ID Variables - Checking the Export Detailed data box will export a csv file with detailed data related to the underlying LityxIQ modeling process. It will include, for each row in the modeling dataset, a binary flag for each iteration showing whether it was part of the model building or validation dataset, as well as its transformed value (if transformations options were selected). In the Filename box, enter a template for the filename to be created. Leave it blank for a default filename to be used. Or, you can manually enter the filename, and use the templates [n] for the model name, [v] for the version, and [s] for subversion, or use configuration variables. If this option is selected, you will also need to select the variables that will be used to uniquely identify each row in the modeling dataset, using the Record ID Variables dropdown selection.
- File Manager Folder - If either of the above two exporting options were selected, you will select which folder in the File Manager the exported files will be saved.