K-Nearest Neighbours

Boosting

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

Random Forest

An ensemble learning method that constructs multiple decision trees during training and outputs the mode of their predictions.

Gradient Boosting

An ensemble technique that builds models sequentially, with each new model correcting residual errors of the combined ensemble.

XGBoost

An optimised distributed gradient boosting library designed for speed and performance in machine learning competitions and production.

Decision Tree

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

Support Vector Machine

A supervised learning algorithm that finds the optimal hyperplane to separate different classes in high-dimensional space.

Naive Bayes

A probabilistic classifier based on applying Bayes' theorem with the assumption of independence between features.

Linear Regression

A statistical method modelling the relationship between a dependent variable and one or more independent variables using a linear equation.

Logistic Regression

A classification algorithm that models the probability of a binary outcome using a logistic function.

Polynomial Regression

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

Tabular Deep Learning

The application of deep neural networks to structured tabular datasets, competing with traditional methods like gradient boosting through specialised architectures and regularisation.

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

Continuous observation of deployed machine learning models to detect performance degradation, data drift, anomalous predictions, and infrastructure issues in production.

A clustering method that builds a tree-like hierarchy of clusters through successive merging or splitting of groups.

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

A machine learning approach where a model is simultaneously trained on multiple related tasks to improve generalisation.

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

Identifying data points, events, or observations that deviate significantly from the expected pattern in a dataset.

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