Note
This command is available with the Predictive Analytics Module. Click here for more information about how to activate the module.
Choose the method or formula of your choice.
The confusion matrix contains results about the classification accuracy of the model. In most cases, the classification for a row is the response level with the highest predicted probability. For example, with a binary response, the classification for the row is the event category when the predicted probability of the event exceeds 0.50. However, for a binary response, you can specify a threshold other than 0.50.
Count
When there are no weights, the counts and the sample sizes are the same.
Weighted count
In the weighted case, the weighted count is the sum of the weights for a
category. Use the weights to calculate percentages and rates. Consider the
following simple example:
| Response level | Predicted level | Weight |
|---|---|---|
| Yes | Yes | 0.1 |
| Yes | Yes | 0.2 |
| Yes | No | 0.3 |
| Yes | No | 0.4 |
| No | No | 0.5 |
| No | No | 0.6 |
| No | Yes | 0.7 |
| No | Yes | 0.8 |
This table provides the following statistics
| Actual class | Weighted count | Predicted class = Yes | Predicted Class = No | Percent correct |
|---|---|---|---|---|
| Yes | 0.1 + 0.2 + 0.3 + 0.4 = 1 | 0.1 + 0.2 = 0.3 | 0.3 + 0.4 = 0.7 | 0.3 / (0.3 + 0.7) ×100 = 30.00% |
| No | 0.5 + 0.6 + 0.7 + 0.8 = 2.6 | 0.7 + 0.8 = 1.5 | 0.5 + 0.6 = 1.1 | 1.1 / (1.5 + 1.1) × 100 = 42.31% |
| All | 1 + 2.6 = 3.6 | 0.3 + 1.5 = 1.8 | 0.7 + 1.1 = 1.8 | (0.3 + 1.1) / 3.6 × 100 = 38.89% |
True positive rate (sensitivity or power)
False positive rate (type I error)
False negative rate (type II error)
True negative rate (specificity)
