Supervised ML

165 methods in this family.

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Active LearningActive learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informa Active learning BoostingActive Learning Boosting combines the query-driven label acquisition of active learning with the weighted-ensemble logic of boosting algorithms such as AdaBoost. The model iterativ Active learning Decision treeActive learning with a decision tree combines the interpretable structure of a CART-style tree with a query strategy that selects the most informative unlabeled instances for human Active Learning Federated LearningFederated Active Learning combines the annotation-efficiency of active learning with the privacy-preserving decentralization of federated learning. A shared global model is trained Active learning Gaussian mixture modelActive Learning Gaussian Mixture Model combines an iterative query strategy with a Gaussian Mixture Model learner. The algorithm selects the most informative unlabeled points — typ Active Learning Gradient BoostingActive Learning Gradient Boosting combines the powerful predictive accuracy of gradient boosted trees with an active learning loop that selects the most informative unlabeled examp

All methods 165

Active Learning Active learning Boosting Active learning Decision tree Active Learning Federated Learning Active learning Gaussian mixture model Active Learning Gradient Boosting Active learning K-nearest neighbors Active Learning LightGBM Active Learning Linear Regression Active learning One-class SVM Active Learning Self-supervised Learning Active learning Stacking ensemble Active learning Support vector machine Active Learning Voting Ensemble AdaBoost Boosting Boosting Ensemble Borda Count Aggregation CatBoost Collaborative Filtering Conformal Prediction Decision Tree Dempster-Shafer Fusion Emerging Pattern Mining Ensemble Active Learning Ensemble Decision Tree Ensemble Federated Learning Ensemble Few-shot learning Ensemble Gaussian Mixture Model Ensemble Gaussian Process Ensemble Gradient Boosting Ensemble K-nearest neighbors Ensemble Metric Learning Ensemble Naive Bayes Ensemble One-class SVM Ensemble Online Learning Ensemble Self-supervised Learning Ensemble Semi-supervised Learning Ensemble Support Vector Machine Ensemble Transfer Learning Extra Trees Few-shot Learning FP-Growth Generalized Additive Model Independent Component Analysis Isomap K-Nearest Neighbors Label Propagation LightGBM Linear Discriminant Analysis Linear Regression (ML)LOESS Majority Voting MARS Matrix Completion Metric Learning Multi-layer Perceptron Naive Bayes Non-negative Matrix Factorization Online Active learning Online Boosting Online Decision Tree Online Federated Learning Online Few-shot Learning Online FP-growth Online Gaussian Mixture Model Online Gaussian Process Online Gradient Boosting Online K-nearest neighbors Online Learning Online LightGBM Online Linear Regression Online Metric Learning Online Naive Bayes Online One-class SVM Online Random Forest Online Self-supervised Learning Online Semi-supervised learning Online Support Vector Machine Online Transfer learning Online Voting Ensemble Out-of-Distribution Detection Partial Least Squares Policy Gradient Q-Learning Quadratic Discriminant Analysis Random Forest Regression Splines Regularized Boosting Regularized CatBoost Regularized Decision Tree Regularized Federated Learning Regularized Few-Shot Learning Regularized Gaussian Process Regularized Gradient Boosting Regularized k-nearest neighbors Regularized Naive Bayes Regularized Online Learning Regularized random forest Regularized semi-supervised learning Regularized Support Vector Machine Regularized Transfer Learning Robust Active Learning Robust Boosting Robust Decision Tree Robust Federated Learning Robust Gaussian Mixture Model Robust Gaussian Process Robust Metric Learning Robust One-class SVM Robust Online Learning Robust Random Forest Robust Stacking Ensemble Robust Support Vector Machine Robust Voting Ensemble Rule Induction Self-supervised Active Learning Self-supervised Boosting Self-supervised Decision Tree Self-supervised Federated learning Self-supervised Few-shot Learning Self-supervised Gaussian Process Self-supervised Gradient Boosting Self-supervised K-nearest neighbors Self-supervised Learning Self-supervised LightGBM Self-supervised Metric learning Self-supervised One-class SVM Self-supervised Random Forest Self-supervised Stacking Ensemble Self-supervised Support Vector Machine Self-supervised Transfer learning Semi-supervised Active Learning Semi-supervised Boosting Semi-supervised CatBoost Semi-supervised Decision Tree Semi-supervised Federated learning Semi-supervised Few-shot Learning Semi-supervised FP-growth Semi-supervised Gaussian Mixture Model Semi-supervised Gaussian Process Semi-supervised Gradient Boosting Semi-supervised K-nearest neighbors Semi-supervised Learning Semi-supervised LightGBM Semi-supervised Linear Regression Semi-supervised Metric Learning Semi-supervised Naive Bayes Semi-supervised One-class SVM Semi-supervised Online Learning Semi-supervised Random Forest Semi-supervised Stacking Ensemble Semi-supervised Support Vector Machine Semi-supervised Transfer Learning Semi-supervised Voting Ensemble Semi-supervised XGBoost Sequential Pattern Mining Stacked Generalization Stacking Stochastic Gradient Descent Support Vector Machine Support Vector Regression Transfer Learning Voting Ensemble XGBoost