This table provides values similar to those in the Prediction Success table, but instead of summing probabilities, the estimated values based on a threshold value are summed instead. Rather than just one threshold however, several thresholds ranging from a user specified low to high value are displayed in user specified increments. This allows the user to compare several success scenarios using different threshold values, to aid in the choice of an ideal threshold.
It might be supposed that the ideal threshold value is the one that maximizes the number of correctly classified observations. However, subjective business considerations may be applied by looking at all of the success values. It may be that wrong predictions in one direction (say estimate 1 when the actual value is 0) may be more tolerable than in the other direction (estimate 0 when the actual value is 1). One may, for example, mind less overlooking fraudulent behavior than wrongly accusing someone of fraud.
The following is an example of a logistic regression multi-threshold success table.
Threshold Probability | Actual Response, Estimate Response | Actual Response, Estimate Non-Response | Actual Non-Response, Estimate Response | Actual Non-Response, Estimate Non-Response |
---|---|---|---|---|
0.0000 | 375 | 0 | 372 | 0 |
0.0500 | 375 | 0 | 326 | 46 |
0.1000 | 374 | 1 | 231 | 141 |
0.1500 | 372 | 3 | 145 | 227 |
0.2000 | 367 | 8 | 93 | 279 |
0.2500 | 358 | 17 | 59 | 313 |
0.3000 | 354 | 21 | 46 | 326 |
0.3500 | 347 | 28 | 38 | 334 |
0.4000 | 338 | 37 | 32 | 340 |
0.4500 | 326 | 49 | 27 | 345 |
0.5000 | 318 | 57 | 27 | 345 |
0.5500 | 304 | 71 | 26 | 346 |
0.6000 | 296 | 79 | 24 | 348 |
0.6500 | 287 | 88 | 22 | 350 |
0.7000 | 279 | 96 | 21 | 351 |
0.7500 | 270 | 105 | 19 | 353 |
0.8000 | 258 | 117 | 18 | 354 |
0.8500 | 245 | 130 | 16 | 356 |
0.9000 | 222 | 153 | 12 | 360 |
0.9500 | 187 | 188 | 10 | 362 |