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Predictive Maintenance Model Training Summary |
Best Model Selected: AdaBoost |
Model Performance Table (Sorted by F1, then Recall): |
Model Best_Params Accuracy Precision Recall F1 ROC_AUC |
AdaBoost {'learning_rate': 0.1, 'n_estimators': 100} 0.640133 0.648687 0.928513 0.763777 0.673954 |
GradientBoosting {'learning_rate': 0.05, 'max_depth': 3, 'n_estimators': 100} 0.660097 0.682410 0.855801 0.759333 0.693509 |
DecisionTree {'max_depth': 5, 'min_samples_split': 2} 0.649603 0.666667 0.881536 0.759191 0.669561 |
LogisticRegression {'C': 1} 0.650371 0.669699 0.872141 0.757630 0.680083 |
RandomForest {'max_depth': 10, 'n_estimators': 100} 0.651651 0.680026 0.838644 0.751052 0.682827 |
Bagging {'n_estimators': 100} 0.632455 0.680585 0.779003 0.726476 0.656016 |
Classification Report: |
precision recall f1-score support |
Normal 0.57 0.16 0.24 1459 |
Maintenance Required 0.65 0.93 0.76 2448 |
accuracy 0.64 3907 |
macro avg 0.61 0.54 0.50 3907 |
weighted avg 0.62 0.64 0.57 3907 |
Confusion Matrix: |
[[ 228 1231] |
[ 175 2273]] |
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