fig3

Figure 3. General classification of ML models. ML: Machine learning; LR: linear regression; RF: random forest; DT: decision tree; SVR: support vector regression; XGBoost: eXtreme gradient boosting; NB: naive Bayes; KNN: K-nearest neighbors; SVM: support vector machine; MLP: multilayer perceptron; OPTICS: ordering points to identify the clustering structure; DBSCAN: density-based spatial clustering of applications with noise; PCA: principal component analysis; LDA: linear discriminant analysis; NMF: non-negative matrix factorization; Isomap: isometric mapping; LLE: locally linear embedding; VAT: virtual adversarial training; LPA: label propagation algorithm; S3VM: semi-supervised SVM; LapRLS: laplacian regularized least squares; GAN: generative adversarial networks; VAE: variational autoencoders; AR: autoregressive models; HMM: hidden Markov model; SARSA: state-action-reward-state-action; DQN: deep Q-networks; PPO: proximal policy optimization; SAC: soft actor-critic.