Output Summary Schema
This table is displayed on the screen.
| Column | Data Type | Description |
|---|---|---|
| model_id | VARCHAR | [Column appears only if multiple models are trained.] Integer, starting with 0, identifying model. |
| summary | VARCHAR | Model data:
|
| between_cluster_error | DOUBLE PRECISION | Sum of squared distances of centroids to global mean, where squared distance of each mean to global mean is multiplied by number of data points in cluster. |
| total_within_cluster_error | DOUBLE PRECISION | Sum of all within_cluster_ss values. |
| pseudo_f | DOUBLE PRECISION | Value given by this formula: (between_cluster_errror / (K - 1)) / (total_within_cluster_error / (N - K)) where N is total number of data points, or total weight if points are weighted, and K is number of clusters. |
OutputTable Schema
| Column | Data Type | Description |
|---|---|---|
| model_id | INTEGER | [Column appears only if multiple models are trained.] Model identifier. |
| cluster_id | INTEGER | Cluster identifier assigned by function. |
| numerical_attribute | DOUBLE PRECISION | [Column appears once for each numerical attribute.] Name of numerical attribute. |
| categorical_attribute | VARCHAR | [Column appears once for each categorical attribute and for each numerical attribute specified by NumericAsCategorical.] Name of attribute. |
| within_cluster_ss | DOUBLE PRECISION | Total distance summed over all points in cluster, between point and cluster center, as calculated by distance metric. |
| cluster_weight | DOUBLE PRECISION | Total weight of data points assigned to cluster. |
| distance_metric | VARCHAR | Value of Distance argument in function call (copied to output table so that you need not specify it again when calling KModesPredict). |
| category_weights | VARCHAR | Value of CategoryWeights argument in function call (copied to output table so that you need not specify it again when calling KModesPredict). |