In Part G: To predict whether the application will be accepted or not, you converted the predicted probabilities into class labels yes with the following condition: probs >.5="yes". Choose the correct statement from below regarding the confusion matrix and overall fraction of correct predictions. The Logistic model incorrectly predicted the result of an application about 98.17% of the times. The fraction of correct predictions for the model is 98.17% The Logistic model correctly predicted the result of an application about 97.8% of the times. Question 8 (5 points) Listen In Part G: To predict whether the application will be accepted or not, you converted the predicted probabilities into class labels yes with the following condition: probs >.5="yes". Choose the correct statement from below from the confusion matrix: Out of 295 actual credit card rejections, the model incorrectly predicted rejection 293 times or 99.3% (Very high) Out of 1017 total actual credit card acceptance, 995 were predicted as rejected by the model or 97.85% (very high). Out of 295 actual credit card rejections, the model incorrectly approved 2 applications (false positive), Out of 1017 total actual credit card acceptance, 22 were predicted as acceptance by the model.

In Part G: To predict whether the application will be accepted or not, you converted the predicted probabilities into class labels yes with the following condition: probs >.5="yes". Choose the correct statement from below regarding the confusion matrix and overall fraction of correct predictions. The Logistic model incorrectly predicted the result of an application about 98.17% of the times. The fraction of correct predictions for the model is 98.17% The Logistic model correctly predicted the result of an application about 97.8% of the times. Question 8 (5 points) In Part G: To predict whether the application will be accepted or not, you converted the predicted probabilities into class labels yes with the following condition: probs >.5= "yes". Choose the correct statement from below from the confusion matrix: Out of 295 actual credit card rejections, the model incorrectly predicted rejection 293 times or 99.3% (Very high) Out of 1017 total actual credit card acceptance, 995 were predicted as rejected by the model or 97.85% (very high). Out of 295 actual credit card rejections, the model incorrectly approved 2 applications (false positive), Out of 1017 total actual credit card acceptance, 22 were predicted as acceptance by the model