Logistic 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.

Linear Regression

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

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

A learning approach that combines a small amount of labelled data with a large amount of unlabelled data during training.

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

A machine learning approach where models discover patterns and structures in data without labelled examples.

A machine learning approach where the algorithm interactively queries a user or oracle to label new data points.

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

A subset of the training data used to compute a gradient update during stochastic gradient descent.

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