The ConfusionMatrix function returns a success message and creates 3 output tables:
- output_table_1, a confusion matrix (also called a contingency table)
- output_table_2, which contains overall statistics
-
output_table_3, which contains statistics for each class
ConfusionMatrix Output Table 1 (Confusion Matrix) Schema Column Name Data Type Description observation/predict VARCHAR One row for each unique value of observed_column in the input table. predicted_class INTEGER The number of times that items in the observed_column were classified as predicted_class. ConfusionMatrix Output Table 2 (Overall Statistics) Schema Column Name Data Type Description key VARCHAR Each row contains one of the following statistic names: - Accuracy
- 95% CI
- Null Error Rate
- P-Value [Acc > NIR]
- Kappa
- McNemar Test P-Value
value DOUBLE PRECISION Values of the statistics.
The schema of output_table_3 depends on the number of classes.
| Column Name | Data Type | Description |
|---|---|---|
| key | VARCHAR | Each row contains one of the following statistic names:
|
| value | DOUBLE PRECISION | Values of the statistics. |
| Column Name | Data Type | Description |
|---|---|---|
| key | VARCHAR | Each row contains one of the following statistic names:
|
| class:expect_class | DOUBLE PRECISION | Values of the statistics for the class expect_class. If you specify the Classes argument, there is one column for each specified value. Otherwise, there is one column for each unique value in the observed column of the input table. |