Linear Regression

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.

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.

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.

Combining deep neural networks with reinforcement learning to enable agents to learn complex decision-making from raw sensory input.

The algorithm for computing gradients of the loss function with respect to network weights, enabling neural network training.

A machine learning paradigm where models are trained on labelled data, learning to map inputs to known outputs.

Uniform Manifold Approximation and Projection — a dimensionality reduction technique for visualisation and general non-linear reduction.

A resampling technique that partitions data into subsets, training on some and validating on others to assess model generalisation.

A machine learning method where models are incrementally updated as new data arrives, rather than being trained in batch.

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

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