Ensemble Methods

Machine Learning

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.

Supervised Learning

A machine learning paradigm where models are trained on labelled data, learning to map inputs to known outputs.

Unsupervised Learning

A machine learning approach where models discover patterns and structures in data without labelled examples.

Reinforcement Learning

A machine learning paradigm where agents learn optimal behaviour through trial and error, receiving rewards or penalties.

Multi-Task Learning

A machine learning approach where a model is simultaneously trained on multiple related tasks to improve generalisation.

Online Learning

A machine learning method where models are incrementally updated as new data arrives, rather than being trained in batch.

Batch Learning

Training a machine learning model on the entire dataset at once before deployment, as opposed to incremental updates.

Active Learning

A machine learning approach where the algorithm interactively queries a user or oracle to label new data points.

Ensemble Learning

Combining multiple machine learning models to produce better predictive performance than any single model.

Feature Selection

The process of identifying and selecting the most relevant input variables for a machine learning model.

Epoch

One complete pass through the entire training dataset during the machine learning model training process.

Model Serialisation

The process of converting a trained model into a format that can be stored, transferred, and later reconstructed for inference.

A learning approach that combines a small amount of labelled data with a large amount of unlabelled data during training.

The infrastructure and processes for deploying trained machine learning models to production environments for real-time predictions.

A technique where knowledge gained from training on one task is applied to a different but related task.

A simple algorithm that classifies data points based on the majority class of their k closest neighbours in feature space.

A regularised regression technique that adds an L2 penalty term to prevent overfitting by constraining coefficient magnitudes.

An optimised distributed gradient boosting library designed for speed and performance in machine learning competitions and production.

A probabilistic classifier based on applying Bayes' theorem with the assumption of independence between features.

Learning to learn — algorithms that improve their learning process by leveraging experience from multiple learning episodes.