Given the following Regression Model of the odds of having high blood pressure from Lavie et al. (BMJ, 2000) surveyed adults referred to a sleep clinic
Risk factor | Coefficient | p-value |
---|---|---|
Age ( years) | 0.805 | 0.04 |
Sex (male) | 0.161 | 0.03 |
BMI () | 0.332 | 0.04 |
Apnoe Index ( units) | 0.116 | 0.23 |
You will be able to
(default ==True ) |
balance |
|
---|---|---|
1 | 4000 | 0.667 |
0 | 2000 | 0.587 |
... | ... |
Predicted | Values | Total | ||
---|---|---|---|---|
Negative (0) | Positive (1) | |||
Actual | Negative (0) | True Negative () | False Positive () | |
Values | Positive (1) | False Negative () | True Positive () | |
Total |
Predicted | Values | Total | ||
---|---|---|---|---|
Negative (0) | Positive (1) | |||
Actual | Negative (0) | True Negative () | False Positive () | |
Values | Positive (1) | False Negative () | True Positive () | |
Total |
Predicted | Values | Total | ||
---|---|---|---|---|
Negative (0) | Positive (1) | |||
Actual | Negative (0) | True Negative () | False Positive () | |
Values | Positive (1) | False Negative () | True Positive () | |
Total |
Predicted | Values | Total | ||
---|---|---|---|---|
Negative (0) | Positive (1) | |||
Actual | Negative (0) | True Negative () | False Positive () | |
Values | Positive (1) | False Negative () | True Positive () | |
Total |
Predicted | Values | Total | ||
---|---|---|---|---|
Negative (0) | Positive (1) | |||
Actual | Negative (0) | True Negative () | False Positive () | |
Values | Positive (1) | False Negative () | True Positive () | |
Total |
has Corona | test result | Classification for threshold 0.5 | Error-Type |
---|---|---|---|
0 | 0.4 | 0 | |
1 | 0.9 | 1 | |
0 | 0.7 | 1 | |
1 | 0.7 | 1 | |
0 | 0.3 | 0 | |
1 | 0.4 | 0 |
Predicted | Values | Total | ||
---|---|---|---|---|
Negative (0) | Positive (1) | |||
Actual | Negative (0) | |||
Values | Positive (1) | |||
Total |
Task
has Corona | test result | Classification for threshold ... | Error-Type |
---|---|---|---|
0 | 0.4 | ||
1 | 0.9 | ||
0 | 0.7 | ||
1 | 0.7 | ||
0 | 0.3 | ||
1 | 0.4 |
has Corona | test result | Classification for threshold 0.4 | Error-Type |
---|---|---|---|
0 | 0.4 | 1 | |
1 | 0.9 | 1 | |
0 | 0.7 | 1 | |
1 | 0.7 | 1 | |
0 | 0.3 | 0 | |
1 | 0.4 | 1 |
Predicted | Values | Total | ||
---|---|---|---|---|
Negative (0) | Positive (1) | |||
Actual | Negative (0) | |||
Values | Positive (1) | |||
Total |
Predicted | Values | Total | ||
---|---|---|---|---|
Negative (0) | Positive (1) | |||
Actual | Negative (0) | |||
Values | Positive (1) | |||
Total |
What proportion identifications were correct?
How often do we hit the target?
What proportion of positive identifications was actually correct?
Of everything we predict to be positive, how many are really positive?
What proportion of actual positives was identified correctly?
How many of the positives do we find?
10 minutes
Corona Test I
Predicted | Values | Total | ||
---|---|---|---|---|
Negative (0) | Positive (1) | |||
Actual | Negative (0) | |||
Values | Positive (1) | |||
Total |
Corona Test II
Predicted | Values | Total | ||
---|---|---|---|---|
Negative (0) | Positive (1) | |||
Actual | Negative (0) | |||
Values | Positive (1) | |||
Total |
Corona Test I
Predicted | Values | Total | ||
---|---|---|---|---|
Negative (0) | Positive (1) | |||
Actual | Negative (0) | |||
Values | Positive (1) | |||
Total |
Corona Test II
Predicted | Values | Total | ||
---|---|---|---|---|
Negative (0) | Positive (1) | |||
Actual | Negative (0) | |||
Values | Positive (1) | |||
Total |
Predicted | Values | Total | ||
---|---|---|---|---|
Negative (0) | Positive (1) | |||
Actual | Negative (0) | |||
Values | Positive (1) | |||
Total |
Given this data. Is it hard to train an model with high accuracy (high share of correct predictions)?
Predicted | Values | Total | ||
---|---|---|---|---|
Negative (0) | Positive (1) | |||
Actual | Negative (0) | |||
Values | Positive (1) | |||
Total |