Naive Bayes

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.

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 technique that decomposes a matrix into constituent matrices, widely used in recommendation systems and dimensionality reduction.

A recommendation technique that makes predictions based on the collective preferences and behaviour of many users.

The infrastructure and processes for deploying trained machine learning models to production environments for real-time predictions.

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

Combining multiple machine learning models to produce better predictive performance than any single model.

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

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

The process of adjusting a model's predicted probabilities so they accurately reflect the true likelihood of outcomes, essential for risk-sensitive decision-making.