Methods and formulas for the misclassification table in Fit Model and Discover Key Predictors with TreeNet® Classification

Note

Choose the method or formula of your choice.

The misclassification table 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 Misclassed Predicted Class = No Percent correct
Yes 0.1 + 0.2 + 0.3 + 0.4 = 1 0.1 + 0.2 = 0.3 ≈ 0 0.3 + 0.4 = 0.7 ≈ 1 (0.3 / 1.0) ×100 = 30%
No 0.5 + 0.6 + 0.7 + 0.8 = 2.6 ≈ 3 0.7 + 0.8 = 1.5 ≈ 2 0.5 + 0.6 = 1.1 ≈ 1 1.1 / 2.6) × 100 = 42.31%
All 1 + 2.6 = 3.6 ≈ 4 0.3 + 1.5 = 1.8 ≈ 2 0.7 + 1.1 = 1.8 ≈ 2 (0.3 + 1.1) / 3.6 × 100 = 38.89%

% Error

In the weighted case, use weighted counts in place of counts.