Machine learning

Random Forest

Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.

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Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI: 10.1023/A:1010933404324 ↗
James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 8). Springer. ISBN: 978-1-4614-7138-7

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Active learning Decision tree Active Learning Gradient Boosting Active Learning LightGBM Active Learning Linear Regression Active Learning Logistic Regression Active learning Support vector machine AdaBoost Attention Mechanism Bagging Bagging Ensemble Bayesian Bagging Bayesian Decision Tree Bayesian k-nearest neighbors Bayesian LightGBM Bayesian Random Forest Bayesian XGBoost BERT Fine-Tuning Bidirectional RNN Boosting Capsule Network CatBoost CNN Image Classification Convolutional Neural Network DBSCAN Decision Tree Deep Reinforcement Learning DeepAR Digital Soil Mapping Dilated CNN Double Machine Learning Elastic Net Ensemble Active Learning Ensemble Apriori Algorithm Ensemble Decision Tree Ensemble Gaussian Mixture Model Ensemble Gaussian Process Ensemble Gradient Boosting Ensemble Isolation Forest Ensemble K-nearest neighbors Ensemble Linear Regression Ensemble Logistic Regression Ensemble Metric Learning Ensemble Naive Bayes Ensemble Online Learning Ensemble Self-supervised Learning Ensemble Support Vector Machine Ensemble Transfer Learning Explainable Decision Tree Explainable Extra Trees Explainable Gradient Boosting Explainable K-Means Explainable K-Nearest Neighbors Explainable LightGBM Explainable Multilayer Perceptron Explainable Naive Bayes Explainable Random Forest Explainable Stacking Ensemble Explainable XGBoost Extra Trees Gaussian Process Geographically Weighted Random Forest GPT Fine-Tuning Gradient Boosting Graph Attention Network Graph Neural Network GRU Informer Isolation Forest K-Means Clustering K-Nearest Neighbors Knowledge Distillation Label Propagation LightGBM LIME Linear Discriminant Analysis Linear Regression (ML)Logistic regression (ML)Longformer / BigBird LoRA and PEFT LSTM Machine learning-assisted epigenome-wide association study Machine learning-assisted genome-wide association study Machine learning-assisted metabolomics analysis Machine learning-assisted microbiome diversity analysis Machine learning-assisted pathway enrichment analysis Machine learning-assisted RNA-seq differential expression Majority Voting Mixture of Experts Multi-layer Perceptron Multilayer Perceptron Multinomial Logistic Regression N-BEATS N-HiTS Naive Bayes Neural Architecture Search Neural ODE Online Bagging Online Random Forest PatchTST Pixel-Based Classification Regularized Decision Tree Regularized random forest Regularized Stacking Ensemble Robust Bagging Robust Decision Tree Robust Gradient Boosting Robust LightGBM Robust Random Forest Robust Stacking Ensemble Robust Voting Ensemble Self-Attention Self-supervised Decision Tree Self-supervised Gradient Boosting Self-supervised Random Forest Self-supervised Stacking Ensemble Semi-supervised Bagging Semi-supervised Decision Tree Semi-supervised FP-growth Semi-supervised Isolation Forest Semi-supervised Random Forest Semi-supervised Stacking Ensemble Semi-supervised Support Vector Machine Semi-supervised XGBoost Sequence-to-Sequence Model SHAP Stacking Stochastic Gradient Descent Support Vector Machine Temporal Fusion Transformer TextCNN Transformer UMAP Vision Transformer Visual Contrastive Learning Voting Ensemble XGBoost

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