Ensemble Learning
Ensemble learning combines multiple models to produce better predictions than any individual model alone. Techniques include bagging, boosting, and stacking, which reduce variance, bias, or both.
Understanding Ensemble Learning
Ensemble learning is a machine learning strategy that combines predictions from multiple models to achieve better accuracy and robustness than any single model alone. Common techniques include bagging, where models like random forests train on different bootstrap samples; boosting, where models like XGBoost sequentially correct previous errors; and stacking, where a meta-model learns to combine base model outputs. Ensembles reduce variance, bias, or both, and consistently rank among top-performing approaches in machine learning competitions and production systems. The diversity of base learners is key: combining decision trees, neural networks, and linear models often yields stronger results than ensembles of identical architectures. Ensemble methods are widely used in fraud detection, medical diagnosis, and recommendation engines. The concept is related to dropout in neural networks, which can be viewed as implicit ensemble training.
Category
Machine Learning
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Accuracy
Accuracy is a metric that measures the proportion of correct predictions out of total predictions made by a model. While intuitive, accuracy can be misleading on imbalanced datasets where one class dominates.
Active Learning
Active learning is a machine learning approach where the model selectively queries an oracle (often a human) for labels on the most informative data points. This reduces the total amount of labeled data needed to train an accurate model.
Anomaly Detection
Anomaly detection is the identification of data points, events, or patterns that deviate significantly from expected behavior. AI-based anomaly detection is used in fraud prevention, cybersecurity, and industrial monitoring.
AutoML
Automated Machine Learning (AutoML) is the process of automating the end-to-end pipeline of applying machine learning, including feature engineering, model selection, and hyperparameter tuning. AutoML democratizes AI by reducing the expertise required.
Bagging
Bagging (Bootstrap Aggregating) is an ensemble technique that trains multiple models on random subsets of training data and combines their predictions. Random Forest is the most well-known bagging-based algorithm.
Bayesian Network
A Bayesian network is a probabilistic graphical model that represents variables and their conditional dependencies using a directed acyclic graph. It enables reasoning under uncertainty and causal inference.
Bias-Variance Tradeoff
The bias-variance tradeoff is the fundamental tension in machine learning between model simplicity (high bias) and model flexibility (high variance). Optimal models balance underfitting and overfitting to generalize well to new data.
Binary Classification
Binary classification is a supervised learning task where the model assigns inputs to one of exactly two categories. Spam detection (spam vs. not spam) and medical diagnosis (positive vs. negative) are common examples.