Feature Store

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 situation where the distribution of classes in a dataset is significantly skewed, with some classes vastly outnumbering others.

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

The balance between a model's ability to minimise bias (error from assumptions) and variance (sensitivity to training data fluctuations).

A statistical method modelling the relationship between a dependent variable and one or more independent variables using a linear equation.

Techniques that add constraints or penalties to a model to prevent overfitting and improve generalisation to new data.

An ensemble technique that sequentially trains models, each focusing on correcting the errors of previous models.

Bootstrap Aggregating — an ensemble method that trains multiple models on random subsets of data and averages their predictions.

A training strategy that presents examples to a model in a meaningful order, typically from easy to hard.