Note: Assess Variable Importance is not available for categorical responses with more than two levels.
Factor values are constructed from observed combinations using a k-nearest neighbors approach, in order to account for correlation. This option treats observed variance and covariance as representative of the covariance structure for your factors. Use this option when you believe that your factors are correlated. Note that this option is sensitive to the number of rows in the data table. If used with a small number of rows, the results can be unreliable.
An importance index that reflects the relative contribution of that factor both alone and in combination with other factors. The Total Effect column is displayed as a bar chart. See Weights.
The Monte Carlo standard error of the Main Effect’s importance index. This is a hidden column that you can access by right-clicking in the report and selecting Columns > Main Effect Std Error. By default, sampling continues until this error is less than 0.01. Details of the calculation are given in Variable Importance Standard Errors. (Not available for Dependent Resampled Inputs option.)
The Monte Carlo standard error of the Total Effect’s importance index. This is a hidden column that you can access by right-clicking in the report and selecting Columns > Total Effect Std Error. By default, sampling continues until this error is less than 0.01. Details of the calculation are given in Variable Importance Standard Errors. (Not available for Dependent Resampled Inputs option.)
A plot that shows the Total Effect indices, located to the right of the final column. You can deselect or reselect this plot by right-clicking in the report and selecting Columns > Weights.
The Marginal Model Plots report (see Figure 2.36) shows a matrix of plots, with a row for each response and columns for the factors. The factors are ordered according to the size of their overall Total Effect importance indices.