Feature Engineering

Lasso Regression

A regularised regression technique that adds an L1 penalty, enabling feature selection by driving some coefficients to zero.

Data Augmentation

Techniques that artificially increase the size and diversity of training data through transformations like rotation, flipping, and cropping.

Class Imbalance

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

SMOTE

Synthetic Minority Over-sampling Technique — a method for addressing class imbalance by generating synthetic examples of the minority class.

Label Noise

Errors or inconsistencies in the annotations of training data that can degrade model performance and lead to unreliable predictions if not properly addressed.

A machine learning paradigm where models learn from a continuous stream of data, accumulating knowledge over time without forgetting previously learned information.

A mathematical framework for modelling sequential decision-making where outcomes are partly random and partly controlled.

Unsupervised learning technique that groups similar data points together based on inherent patterns without predefined labels.

A versioned catalogue of trained machine learning models with metadata, lineage, and approval workflows, enabling reproducible deployment and governance at enterprise scale.

Machine learning techniques that combine multiple models to produce better predictive performance than any single model, including bagging, boosting, and stacking approaches.

A partitioning algorithm that divides data into k clusters by minimising the distance between points and their cluster centroids.

The algorithm for computing gradients of the loss function with respect to network weights, enabling neural network training.

A mathematical function that measures the difference between predicted outputs and actual target values during model training.