Label Noise

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.

SMOTE

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

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

A hyperparameter that controls how much model parameters are adjusted with respect to the loss gradient during training.

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

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

A tree-structured model where internal nodes represent feature tests, branches represent outcomes, and leaves represent predictions.

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

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

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