Compare the Block 1 Classification Table, which shows how well the 11X model classifies the data, to the Block 0 Classification Table, which shows the result of predicting that customers reject all loan offers. How many more loan offers (a number, not a percentage) does the 11X model correctly classify? Think of Block 0 results as establishing the baseline for evaluating the accuracy of any model. If you tell me you can predict coin flips, you have to beat 50 (not zero) percent accuracy. The Block 0 results are produced by applying the same prediction (whichever outcome occurred most in the data) to every case. Correct predictions include correctly predicting acceptance and also correctly predicting rejection of loan offers.
To determine the number of additional loan offers that the 11X model correctly classifies compared to the Block 0 results, we need to compare the correct predictions in both cases.
In the Block 0 Classification Table, all customers are predicted to reject all loan offers. This means that if any customers do accept loan offers, those predictions would be incorrect.
In contrast, the Block 1 Classification Table shows the classification results of the 11X model, which takes into account the specific data and makes predictions accordingly. Therefore, it is expected to have a higher accuracy compared to the Block 0 predictions.
To calculate the additional correct predictions made by the 11X model, we need to find the difference between the correct predictions in the Block 1 Classification Table and the Block 0 Classification Table.
The question does not provide the specific data or the values in each table, so we cannot determine the exact number of additional correct predictions.