Attribute Analysis

Attribute Analysis runs an error analysis on the selected input columns and determines which columns combined produce the least error, and ranks the importance of each variable in the calculation. After the process runs, the results are displayed in two charts: Mean Absolute Errors for Columns, and Importance per Variable. Typically the attribute analysis will narrow the input columns to a number under 10. You can then adjust the input columns to those the analysis determined were most important.

To run attribute analysis, the application takes the two input columns (variables) that are found to be most important, and adds one variable at a time. When the Mean Absolute Error line flattens out, it means that the error has basically reached its smallest value, so using additional variables in the calculation adds little to the accuracy of the calculation. The next step is to remove all input columns except the ones the application determined contributed to reducing the error for the final run.

Once you determine which attributes are most important to the model, save the model as a template. All attributes and hyperparameter values are saved. See Production Prediction Workflow for an example on how to analyze which attributes to use in a model.