From: Machine learning approach for predicting production delays: a quarry company case study
Machine Learning Technique | Accuracy | Delay status | Sensitivity | precision | F-measure |
---|---|---|---|---|---|
Decision Tree (Gini Index) | 0.935 | False | 0.917 | 0.917 | 0.917 |
True | 0.947 | 0.947 | 0.947 | ||
Neural Network—Multilayer perceptron (Z-score normalization) | 0.968 | False | 1 | 0.923 | 0.96 |
True | 0.947 | 1 | 0.973 | ||
Random Forest (Z-score normalization, Information Gain Ratio) | 0.935 | False | 0.917 | 0.917 | 0.917 |
True | 0.947 | 0.947 | 0.947 | ||
Naïve Bayes (Z-score normalization) | 0.613 | False | 0 | – | – |
True | 1 | 0.613 | 0.76 | ||
Logistic Regression (Z-score normalization) | 0.968 | False | 1 | 0.923 | 0.96 |
True | 0.947 | 1 | 0.973 |