Prediction Success Table
The prediction success table is computed using only probabilities and not estimates based on a threshold value. Using an input table that contains known values for the dependent variable, the sum of the probability values π(x) and 1 – π(x), which correspond to the probability that the predicted value is 1 or 0, respectively, are calculated separately for rows with actual value of 1 and 0. See Model Evaluation for more information.
| Column | Description |
|---|---|
| Estimate Response | Sums of the probabilities  that the outcome is 1, summed separately over the observations where the actual outcome is 1 and 0 and then totaled. (Note that this is independent of the threshold value that is used in scoring to determine which probabilities correspond to an estimate of 1 and 0, respectively). |
| Estimate Non-Response | Sums of the probabilities  that the outcome is 0, summed separately over the observations where the actual outcome is 1 and 0 and then totaled. (Note that this is independent of the threshold value that is used in scoring to determine which probabilities correspond to an estimate of 1 and 0, respectively). |
| Actual Total | Sums of the entries in the Estimate Response and Estimate Non-Response columns, across the rows in the prediction success table, which turns out to be the number of actual 0s and 1s and total observations in the training data. |
| Actual Response | Observations in the data where the actual value of the dependent variable is 1. |
| Actual Non-Response | Observations in the data where the actual value of the dependent variable is 0. |
| Estimated Total | Sums of the entries in the Actual Response and Actual Non-Response rows, down the columns in the prediction success table, which turns out to be the sum of the probabilities of estimated 0s and 1s and total observations in the model. |
Multi-Threshold Success Table
This table provides values similar to those in the prediction success table. Instead of summing probabilities, the estimated values based on a threshold value are summed. Several thresholds ranging from a user-specified low to high value are displayed in user specified increments. Use this table to compare several success scenarios using different threshold values, to aid in the choice of an ideal threshold. See Model Evaluation for more information.
| Column | Description |
|---|---|
| Threshold Probability | Provides incremental values of the probability at or above which an observation is to have an estimated value of 1 for the dependent variable. For example, at a threshold of 0.5, a response value of 1 is estimated if the probability predicted by the logistic regression model is greater than or equal to 0.5. You can request the starting, ending and increment values for these thresholds. |
| Actual Response, Estimate Response | Number of observations for which the model estimated a value of 1 for the dependent variable and the actual value of the dependent variable is 1. |
| Actual Response, Estimate Non-Response | Number of observations for which the model estimated a value of 0 for the dependent variable but the actual value of the dependent variable is 1, a “false negative” error case for the model. |
| Actual Non-Response, Estimate Response | Number of observations for which the model estimated a value of 1 for the dependent variable but the actual value of the dependent variable is 0, a “false positive” error case for the model. |
| Actual Non-Response, Estimate Non-Response | Number of observations for which the model estimated a value of 0 for the dependent variable and the actual value of the dependent variable is 0. |
Cumulative Lift Table
The cumulative lift table demonstrates how effective the model is in estimating the dependent variable. It is produced using deciles based on the probability values. The deciles are labeled such that 1 is the highest decile and 10 is the lowest, based on the probability values calculated by logistic regression. That is, decile 1 contains data on the 10% of the observations with the highest estimated probabilities that the dependent variable is 1.
| Column | Description |
|---|---|
| Count | Count of observations in the decile. |
| Response | Count of observations in the decile where the actual value of the dependent variable is 1. |
| Response (%) | Percentage of observations in the decile where the actual value of the dependent variable is 1. |
| Captured Response (%) | Percentage of responses in the decile over all the responses in any decile. |
| Lift | Percentage response in the decile (Pct Response) divided by the expected response, where the expected response is the percentage of response or dependent 1-values over all observations. For example, if 10% of the observations overall have a dependent variable with value 1, and 20% of the observations in decile 1 have a dependent variable with value 1, then the lift value within decile 1 is 2.0, meaning that the model gives a “lift” that is better than chance alone by a factor of two in predicting response values of 1 within this decile. |
| Cumulative Response | Cumulative measure of Response, from decile 1 to this decile. |
| Cumulative Response (%) | Cumulative measure of Pct Response, from decile 1 to this decile. |
| Cumulative Captured Response (%) | Cumulative measure of Pct Captured Response, from decile 1 to this decile. |
| Cumulative Lift | Cumulative measure of Lift, from decile 1 to this decile. |