Dr. Kim has obtained the following classification table from a logistic regression. Compute the misclassification rate. Predicted 1 Total Observed 1 526 56 582 173 504 677 Total 699 560 1,259
Dr. Kim has obtained the following classification table from a logistic regression. Compute the misclassification rate. Predicted 1 Total Observed 1 526 56 582 173 504 677 Total 699 560 1,259
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
A 18.2%
B 15.7%
C 22.2%
D 4.4%
![Dr. Kim has obtained the following classification table from a logistic regression. Compute the misclassification rate.
| | Predicted | | |
|--------------|-----------|----------|-----------|
| | 1 | 0 | Total |
| Observed | | | |
| 1 | 526 | 56 | 582 |
| 0 | 173 | 504 | 677 |
| Total | 699 | 560 | 1,259 |
### Explanation of the Table
This table is a confusion matrix used to evaluate the performance of a classification model, specifically a logistic regression in this case. It compares the predicted classifications against the observed classifications.
- **Rows**: Represent the observed (actual) values.
- **Columns**: Represent the predicted values.
#### Key Elements:
- **True Positives (TP)**: 526
- Instances where the model correctly predicted '1' (Observed '1' and Predicted '1').
- **False Negatives (FN)**: 56
- Instances where the model failed to predict '1' (Observed '1' but Predicted '0').
- **False Positives (FP)**: 173
- Instances where the model incorrectly predicted '1' (Observed '0' but Predicted '1').
- **True Negatives (TN)**: 504
- Instances where the model correctly predicted '0' (Observed '0' and Predicted '0').
#### Misclassification Rate Calculation:
The misclassification rate is the proportion of incorrect predictions made by the model. It is calculated using the formula:
\[ \text{Misclassification Rate} = \frac{\text{FP} + \text{FN}}{\text{Total Observations}} \]
Substitute the values:
\[ \text{Misclassification Rate} = \frac{173 + 56}{1,259} \]
\[ \text{Misclassification Rate} = \frac{229}{1,259} \approx 0.182 \]
Therefore, the misclassification rate is approximately 18.2%.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F2e270ba2-b16a-45cb-8fe7-d13efc897142%2Fe093e0d6-1f4b-484d-9895-5fe71724eb74%2Fz0k8kqj_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Dr. Kim has obtained the following classification table from a logistic regression. Compute the misclassification rate.
| | Predicted | | |
|--------------|-----------|----------|-----------|
| | 1 | 0 | Total |
| Observed | | | |
| 1 | 526 | 56 | 582 |
| 0 | 173 | 504 | 677 |
| Total | 699 | 560 | 1,259 |
### Explanation of the Table
This table is a confusion matrix used to evaluate the performance of a classification model, specifically a logistic regression in this case. It compares the predicted classifications against the observed classifications.
- **Rows**: Represent the observed (actual) values.
- **Columns**: Represent the predicted values.
#### Key Elements:
- **True Positives (TP)**: 526
- Instances where the model correctly predicted '1' (Observed '1' and Predicted '1').
- **False Negatives (FN)**: 56
- Instances where the model failed to predict '1' (Observed '1' but Predicted '0').
- **False Positives (FP)**: 173
- Instances where the model incorrectly predicted '1' (Observed '0' but Predicted '1').
- **True Negatives (TN)**: 504
- Instances where the model correctly predicted '0' (Observed '0' and Predicted '0').
#### Misclassification Rate Calculation:
The misclassification rate is the proportion of incorrect predictions made by the model. It is calculated using the formula:
\[ \text{Misclassification Rate} = \frac{\text{FP} + \text{FN}}{\text{Total Observations}} \]
Substitute the values:
\[ \text{Misclassification Rate} = \frac{173 + 56}{1,259} \]
\[ \text{Misclassification Rate} = \frac{229}{1,259} \approx 0.182 \]
Therefore, the misclassification rate is approximately 18.2%.
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