Skip to main content

Table 2 Performance evaluation of optimal Machine Learning models

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

  1. Optimal model is denoted with the bold font