Table 1.
List of popular machine learning (ML) models.
| ML model | Acronym | Type of learning | Type of problem |
|---|---|---|---|
| Linear regression | N/A | Supervised | Regression |
| Logistic regression | LR | Supervised | Classification |
| Decision trees | DT | Supervised | Regression, classification |
| K‐means | N/A | Unsupervised | Clustering |
| Naive Bayes | NB | Supervised | Classification |
| Support vector machines | SVM | Supervised | Regression, classification |
| K‐nearest neighbors | KNN | Supervised | Regression, classification |
| Ensemble learning modelsa | |||
| Extremely randomized trees [23] | Extra‐trees (ET) | Supervised | Regression, classification |
| Random forests [24] | RF | ||
| Gradient boosting machines | GBM | ||
| eXtreme gradient boosting [25] | XGBoost | ||
| Light gradient boosting machine [26] | LightGBM | ||
| Gradient boosted decision trees | GBDT | ||
| Adaptive boosting [27] | AdaBoost | ||
| Category boosting [28] | CatBoost | ||
a
Ensemble learning is a meta‐learning approach that combines multiple models to make a decision, typically in supervised ML tasks [29].