XGBoost

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.

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.

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.

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

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

A regularisation technique combining L1 and L2 penalties, balancing feature selection and coefficient shrinkage.

Unsupervised learning technique that groups similar data points together based on inherent patterns without predefined labels.

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

A mathematical function that measures the difference between predicted outputs and actual target values during model training.

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