Table 14.
Hyperparameters, training and testing times, and model size of the model used.
| Preset | Prediction speed (obs/sec) | Training time (sec) | Model size (mb) | Hyperparameters | |
|---|---|---|---|---|---|
| Tree | Fine Tree | 199,460.37 | 8.06 | 0.03 | Maximum number of splits: 100; Split criterion: Gini’s diversity index; Surrogate decision splits: Off |
| Medium Tree | 215,682.95 | 6.50 | 0.02 | Maximum number of splits: 20; Split criterion: Gini’s diversity index; Surrogate decision splits: Off | |
| Coarse Tree | 201,316.36 | 4.24 | 0.01 | Maximum number of splits: 4; Split criterion: Gini’s diversity index; Surrogate decision splits: Off | |
| Naive Bayes | Gaussian Naive Bayes | 116,141.60 | 9.87 | 0.04 | Distribution name for numeric predictors: Gaussian; Distribution name for categorical predictors: Not Applicable |
| Kernel Naive Bayes | 65.91 | 2,186.66 | 44.61 | Distribution name for numeric predictors: Kernel; Distribution name for categorical predictors: Not Applicable; Kernel type: Gaussian; Support: Unbounded; Standardize data: Yes | |
| SVM | Linear SVM | 136,027.65 | 716.03 | 0.07 | Kernel function: Linear; Kernel scale: Automatic; Box constraint level: 1; Multiclass coding: One-vs-One; Standardize data: Yes |
| Quadratic SVM | 46,834.80 | 476.87 | 0.85 | Kernel function: Quadratic; Kernel scale: Automatic; Box constraint level: 1; Multiclass coding: One-vs-One; Standardize data: Yes | |
| Cubic SVM | 105,060.76 | 228.82 | 0.39 | Kernel function: Cubic; Kernel scale: Automatic; Box constraint level: 1; Multiclass coding: One-vs-One; Standardize data: Yes | |
| Fine Gaussian SVM | 7,101.95 | 1,857.39 | 1.70 | Kernel function: Gaussian; Kernel scale: 1.6; Box constraint level: 1; Multiclass coding: One-vs-One; Standardize data: Yes | |
| Medium Gaussian SVM | 2,550.18 | 463.74 | 4.77 | Kernel function: Gaussian; Kernel scale: 6.5; Box constraint level: 1; Multiclass coding: One-vs-One; Standardize data: Yes | |
| Coarse Gaussian SVM | 802.98 | 2,022.63 | 18.11 | Kernel function: Gaussian; Kernel scale: 26; Box constraint level: 1; Multiclass coding: One-vs-One; Standardize data: Yes | |
| KNN | Fine KNN | 1,444.46 | 2,020.55 | 11.87 | Number of neighbors: 1; Distance metric: Euclidean; Distance weight: Equal; Standardize data: Yes |
| Medium KNN | 1,107.97 | 2,019.08 | 11.87 | Number of neighbors: 10; Distance metric: Euclidean; Distance weight: Equal; Standardize data: Yes | |
| Coarse KNN | 1,331.39 | 2,018.03 | 11.87 | Number of neighbors: 100; Distance metric: Euclidean; Distance weight: Equal; Standardize data: Yes | |
| Cosine KNN | 1,409.26 | 2,016.72 | 11.87 | Number of neighbors: 10; Distance metric: Cosine; Distance weight: Equal; Standardize data: Yes | |
| Cubic KNN | 225.73 | 2,015.70 | 11.87 | Number of neighbors: 10; Distance metric: Minkowski (cubic); Distance weight: Equal; Standardize data: Yes | |
| Weighted KNN | 1,106.19 | 2,014.63 | 11.87 | Number of neighbors: 10; Distance metric: Euclidean; Distance weight: Squared inverse; Standardize data: Yes | |
| Kernel | SVM Kernel | 2,679.67 | 1,822.93 | 0.18 | Learner:SVM; Number of expansion dimensions:Auto; Regularization strength (Lambda): Auto; Kernel scale: Auto; Multiclass coding: One-vs-One; Standardize data: Yes; Iteration limit: 1,000 |
| Logistic regression Kernel | 2,507.49 | 1,630.32 | 0.18 | Learner:Logistic Regression;Number of expansion dimensions:Auto; Regularization strength (Lambda):Auto; Kernel scale:Auto; Multiclass coding:One-vs-One; Standardize data:Yes; Iteration limit:1,000 | |
| Ensemble | Boosted Trees | 31,365.62 | 1,627.50 | 0.46 | Ensemble method: AdaBoost; Learner type: Decision tree; Maximum number of splits: 20; Number of learners: 30; Learning rate: 0.1; Number of predictors to sample: Select All |
| Bagged Trees | 35,573.27 | 1,626.26 | 0.91 | Ensemble method: Bag; Learner type: Decision tree; Maximum number of splits: 34,559; Number of learners: 30; Number of predictors to sample: Select All | |
| Subspace Discriminant | 7,054.04 | 1,371.97 | 0.46 | Ensemble method: Subspace; Learner type: Discriminant; Number of learners: 30; Subspace dimension: 21 | |
| Subspace KNN | 627.05 | 2,508.81 | 190.03 | Ensemble method: Subspace; Learner type: Nearest neighbors; Number of learners: 30; Subspace dimension: 21 | |
| RUSBoosted Trees | 44,593.85 | 98.08 | 0.46 | Ensemble method: RUSBoost; Learner type: Decision tree; Maximum number of splits: 20; Number of learners: 30; Learning rate: 0.1; Number of predictors to sample: Select All | |
| NN | Narrow Neural Network | 146,509.71 | 256.19 | 0.01 | Number of fully connected layers: 1; First layer size: 10; Activation: Rectified Linear Unit (ReLU); Iteration limit: 1,000; Regularization strength (Lambda): 0; Standardize data: Yes |