Boosting

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.

K-Nearest Neighbours

A simple algorithm that classifies data points based on the majority class of their k closest neighbours in feature 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.

The process of converting a trained model into a format that can be stored, transferred, and later reconstructed for inference.

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

A centralised repository for storing, managing, and serving machine learning features, ensuring consistency between training and inference environments across an organisation.

Training a machine learning model on the entire dataset at once before deployment, as opposed to incremental updates.

A game-theoretic approach to explaining individual model predictions by computing each feature's marginal contribution, based on Shapley values from cooperative game theory.

A mathematical framework for modelling sequential decision-making where outcomes are partly random and partly controlled.

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

Machine learning techniques that combine multiple models to produce better predictive performance than any single model, including bagging, boosting, and stacking approaches.