Data Augmentation

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.

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 recommendation approach that suggests items similar to those a user has previously liked, based on item attributes.

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

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

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

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

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

When a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test data.

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