Polynomial 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.

Logistic Regression

A classification algorithm that models the probability of a binary outcome using a logistic function.

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

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

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

Continuous observation of deployed machine learning models to detect performance degradation, data drift, anomalous predictions, and infrastructure issues in production.

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

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

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

When a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test data.

Regression Analysis

A set of statistical processes for estimating the relationships between dependent and independent variables.