Table 2 Comparison of accuracy and bias metrics for predictive models used
From: Comparing regression modeling strategies for predicting hometime
Model | Root Mean Square Error | Mean Absolute Error | Bias | Minimum Predicted Value | Maximum Predicted Value | Calibration Slope |
|---|---|---|---|---|---|---|
Statistical Methods | ||||||
Linear Regression | 28.82 | 24.13 | -0.26 | -53.74 | 103.37 | 1.00 |
Ordinal Logistic Regression | 28.64 | 23.96 | -0.38 | 0.23 | 84.03 | 1.04 |
Poisson Regression | 29.02 | 24.50 | -0.25 | 2.90 | 144.98 | 0.95 |
Negative Binomial Regression | 30.15 | 25.15 | 0.75 | 2.47 | 189.83 | 0.77 |
Zero Inflated Poisson Regression | 28.47 | 23.68 | -0.31 | 0.17 | 95.59 | 1.04 |
Zero Inflated Negative Binomial Regression | 28.53 | 23.74 | -0.31 | 0.18 | 97.46 | 1.03 |
Cox Proportional Hazards Model | 29.29 | 25.62 | -1.64 | 0.00 | 77.00 | 1.33 |
Hurdle Regression (negative binomial zero distribution, Poisson distribution) | 28.47 | 23.65 | -0.25 | 0.50 | 95.99 | 1.02 |
Machine Learning Methods | ||||||
Random Forests Regression | 28.32 | 23.08 | -0.40 | 0.04 | 85.83 | 0.98 |
Bagged Regression Trees | 29.48 | 24.98 | -0.25 | 18.20 | 73.29 | 1.06 |
Support Vector Regression | 29.18 | 21.55 | 2.08 | -17.91 | 91.99 | 0.74 |
Generalized Boosting Machine (Gaussian Distribution, Interaction Depth = 2) | 28.39 | 23.89 | -0.30 | 3.23 | 78.72 | 1.11 |
Generalized Boosting Machine (Poisson Distribution, Interaction Depth = 15) | 27.89 | 22.81 | -0.35 | 3.49 | 83.39 | 1.01 |
Lasso Regression | 28.82 | 24.14 | -0.26 | -53.45 | 103.21 | 1.00 |
Ridge Regression | 28.83 | 24.25 | -0.27 | -50.06 | 101.93 | 1.03 |