Class Imbalance

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.

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 technique where knowledge gained from training on one task is applied to a different but related task.

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 regularisation technique combining L1 and L2 penalties, balancing feature selection and coefficient shrinkage.

A dimensionality reduction technique that transforms data into orthogonal components ordered by the amount of variance they explain.

When a model learns the training data too well, including noise, resulting in poor performance on unseen data.

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

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

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