Update README.md
Browse files
README.md
CHANGED
|
@@ -146,7 +146,7 @@ Since the groups are almost equal, our model can learn from both types of data e
|
|
| 146 |
# Part 8: Classification Model Evaluation & Results
|
| 147 |
In this final analytical stage, I evaluated the three trained classifiers using Confusion Matrices to understand their prediction patterns and error types.
|
| 148 |
### Model Performance Analysis:
|
| 149 |
-
####
|
| 150 |
Performance: The Decision Tree captured the highest number of actual delays (5,989 True Positives).
|
| 151 |
|
| 152 |
Drawback: It suffered from an unacceptably high rate of False Positives (2,974). This means it predicted a delay for nearly 3,000 flights that actually arrived on time, making it too "trigger-happy" and unreliable for a stress-free passenger experience.
|
|
|
|
| 146 |
# Part 8: Classification Model Evaluation & Results
|
| 147 |
In this final analytical stage, I evaluated the three trained classifiers using Confusion Matrices to understand their prediction patterns and error types.
|
| 148 |
### Model Performance Analysis:
|
| 149 |
+
#### Desicion Tree
|
| 150 |
Performance: The Decision Tree captured the highest number of actual delays (5,989 True Positives).
|
| 151 |
|
| 152 |
Drawback: It suffered from an unacceptably high rate of False Positives (2,974). This means it predicted a delay for nearly 3,000 flights that actually arrived on time, making it too "trigger-happy" and unreliable for a stress-free passenger experience.
|