So if the baseline is 25% survive, then 75% fail, for a sum of 100%. How is it correct to compare that baseline to two numbers (66% and 88%) which don't sum to 100? Is failing different from not surviving?
1. The models predicted survival, and the business survived
2. Predicted survival, but the business failed
3. Predicted failure, but the business survived,
4. Predicted failure, and the business failed
Here's how our results stacked up:
#1. 69% of observations (it wobbles around 66%, but was 69% in the last update)
#2. 31% of observations (notice #1 + #2 = 100%. Out of 100% of the times we predicted survival, we were right 69% and wrong 31%)
#3. 12%
#4. 88% (again, #3 + #4 = 100%. Out of all the times we predicted failure, we were right 88% of the time and wrong 12%).
Again, there's more luck when you predict failure, so it's really around 66% for both positives and negatives when you dig deeper into the stats.
Possible outcomes:
1. The models predicted survival, and the business survived 2. Predicted survival, but the business failed 3. Predicted failure, but the business survived, 4. Predicted failure, and the business failed
Here's how our results stacked up: #1. 69% of observations (it wobbles around 66%, but was 69% in the last update) #2. 31% of observations (notice #1 + #2 = 100%. Out of 100% of the times we predicted survival, we were right 69% and wrong 31%) #3. 12% #4. 88% (again, #3 + #4 = 100%. Out of all the times we predicted failure, we were right 88% of the time and wrong 12%).
Again, there's more luck when you predict failure, so it's really around 66% for both positives and negatives when you dig deeper into the stats.