Ensemble learning

Ensemble learning is a machine learning paradigm where multiple models (often called "weak learners") are trained to solve the same problem and combined to get better results. The main premise behind ensemble learning is that a group of "weak learners" can come together to form a "strong learner," thereby improving the model's accuracy and robustness. This approach can be applied across various areas, including but not limited to, classification, regression, and anomaly detection tasks.

Overview[edit | edit source]

Ensemble methods are techniques that create multiple models and then combine them to produce improved results. Ensemble learning can be achieved through several approaches, the most notable ones being Bagging, Boosting, and Stacking.

Bagging (Bootstrap Aggregating): It involves training multiple models in parallel, each on a random subset of the data (with replacement), and then averaging their predictions. A well-known algorithm that uses bagging is the Random Forest algorithm.

Boosting: This approach trains models sequentially, each trying to correct its predecessor. The most common boosting algorithms include AdaBoost (Adaptive Boosting) and Gradient Boosting.

Stacking: Stacking involves training a new model to combine the predictions of several other models. The base level models are trained based on a complete training set, then a new model is trained on the outputs of the base models as features.

Advantages[edit | edit source]

Ensemble learning methods can lead to more accurate predictions compared to a single model. They are less likely to overfit, especially when models in the ensemble are diverse. Ensembles can also improve the robustness of the system, making it more resilient to new, unseen data.

Disadvantages[edit | edit source]

While ensemble methods can improve model performance, they also come with their own set of challenges. These include increased computational cost, complexity in model interpretation, and the need for careful tuning to avoid overfitting with some methods.

Applications[edit | edit source]

Ensemble learning has been successfully applied in various domains, including but not limited to:

References[edit | edit source]

Machine learning and data mining
Part of a series on
Paradigms Supervised learning Unsupervised learning Online learning Batch learning Meta-learning Semi-supervised learning Self-supervised learning Reinforcement learning Curriculum learning Rule-based learning Quantum machine learning
Problems Classification Generative modeling Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured prediction Feature engineering Feature learning Learning to rank Grammar induction Ontology learning Multimodal learning
Supervised learning (classification • regression) Apprenticeship learning Decision trees Ensembles Bagging Boosting Random forest k-NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Clustering BIRCH CURE Hierarchical k-means Fuzzy Expectation–maximization (EM) DBSCAN OPTICS Mean shift
Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA PGD t-SNE SDL
Structured prediction Graphical models Bayes net Conditional random field Hidden Markov
Anomaly detection RANSAC k-NN Local outlier factor Isolation forest
Artificial neural network Autoencoder Cognitive computing Deep learning DeepDream Feedforward neural network Recurrent neural network LSTM GRU ESN reservoir computing Restricted Boltzmann machine GAN Diffusion model SOM Convolutional neural network U-Net Transformer Vision Mamba Spiking neural network Memtransistor Electrochemical RAM (ECRAM)
Reinforcement learning Q-learning SARSA Temporal difference (TD) Multi-agent Self-play
Learning with humans Active learning Crowdsourcing Human-in-the-loop RLHF
Model diagnostics Coefficient of determination Confusion matrix Learning curve ROC curve
Mathematical foundations Kernel machines Bias–variance tradeoff Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory
Machine-learning venues ECML PKDD NeurIPS ICML ICLR IJCAI ML JMLR
Related articles Glossary of artificial intelligence List of datasets for machine-learning research List of datasets in computer vision and image processing Outline of machine learning
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Artificial intelligence
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Major goals Artificial general intelligence Recursive self-improvement Planning Computer vision General game playing Knowledge reasoning Machine learning Natural language processing Robotics AI safety
Approaches Symbolic Deep learning Bayesian networks Evolutionary algorithms Situated approach Hybrid intelligent systems Systems integration
Applications Projects Deepfake Machine translation Art Healthcare Mental health Government Music Industry Earth sciences Bioinformatics Physics
Philosophy Chinese room Friendly AI Control problem/Takeover Ethics Existential risk Turing test Regulation
History Timeline Progress AI winter AI boom AI era
Glossary Glossary
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Machine learning and data mining
Part of a series on
Paradigms Supervised learning Unsupervised learning Online learning Batch learning Meta-learning Semi-supervised learning Self-supervised learning Reinforcement learning Curriculum learning Rule-based learning Quantum machine learning
Problems Classification Generative modeling Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured prediction Feature engineering Feature learning Learning to rank Grammar induction Ontology learning Multimodal learning
Supervised learning (classification • regression) Apprenticeship learning Decision trees Ensembles Bagging Boosting Random forest k-NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Clustering BIRCH CURE Hierarchical k-means Fuzzy Expectation–maximization (EM) DBSCAN OPTICS Mean shift
Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA PGD t-SNE SDL
Structured prediction Graphical models Bayes net Conditional random field Hidden Markov
Anomaly detection RANSAC k-NN Local outlier factor Isolation forest
Artificial neural network Autoencoder Cognitive computing Deep learning DeepDream Feedforward neural network Recurrent neural network LSTM GRU ESN reservoir computing Restricted Boltzmann machine GAN Diffusion model SOM Convolutional neural network U-Net Transformer Vision Mamba Spiking neural network Memtransistor Electrochemical RAM (ECRAM)
Reinforcement learning Q-learning SARSA Temporal difference (TD) Multi-agent Self-play
Learning with humans Active learning Crowdsourcing Human-in-the-loop RLHF
Model diagnostics Coefficient of determination Confusion matrix Learning curve ROC curve
Mathematical foundations Kernel machines Bias–variance tradeoff Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory
Machine-learning venues ECML PKDD NeurIPS ICML ICLR IJCAI ML JMLR
Related articles Glossary of artificial intelligence List of datasets for machine-learning research List of datasets in computer vision and image processing Outline of machine learning
v t e

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Overview[edit | edit source]

Advantages[edit | edit source]

Disadvantages[edit | edit source]

Applications[edit | edit source]

See Also[edit | edit source]

References[edit | edit source]

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