Problem |
Classification |
Target Column Name |
target |
Model's Name |
RandomForestClassifier |
Accuracy Score |
0.85000 |
Roc Auc curve |
0.850 |
Mean accuracy score of each tested hyperparameter combination |
0.732 |
Range of all accuracy scores of each tested hyperparameter combination |
0.708 - 0.792 |
Standard Deviation of scores |
0.031 |
Standard Deviation < 0.1 * Mean Accuracy scores |
The scores are relatively consistent. |
Classification Report:
|
precision |
recall |
f1-score |
support |
N |
0.838710 |
0.866667 |
0.852459 |
30.00 |
P |
0.862069 |
0.833333 |
0.847458 |
30.00 |
accuracy |
0.850000 |
0.850000 |
0.850000 |
0.85 |
macro avg |
0.850389 |
0.850000 |
0.849958 |
60.00 |
weighted avg |
0.850389 |
0.850000 |
0.849958 |
60.00 |
Roc Auc curve figure:
Overfit Report:
Overfit Report |
The Report is based only on Accuracy |
Train set accuracy score of best pipeline |
0.8661 |
Test set accuracy score of best pipeline |
0.8500 |
Overfit estimation score of the best pipeline |
0.0161 |
Learning Curve scores report |
The Learning Curve is based on Accuracy |
Train set accuracy score of learning curve's last value |
0.87 |
Test set accuracy score of learning curve's last value |
0.78 |
Overfit gap of learning curve's last value |
0.09 |
Learning Curve - Overfitting or Underfitting: