SMOTE

Lasso Regression

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

Feature Engineering

The process of using domain knowledge to create, select, and transform input variables to improve model performance.

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.

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.

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

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

Combining deep neural networks with reinforcement learning to enable agents to learn complex decision-making from raw sensory input.

A controlled experiment comparing two variants to determine which performs better against a defined metric.

An optimisation algorithm that iteratively adjusts parameters in the direction of steepest descent of the loss function.

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

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

A form of regression analysis where the relationship between variables is modelled as an nth degree polynomial.