Cross-Validation

Ridge Regression

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

Elastic Net

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

Overfitting

When a model learns the training data too well, including noise, resulting in poor performance on unseen data.

Underfitting

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

Bias-Variance Tradeoff

The balance between a model's ability to minimise bias (error from assumptions) and variance (sensitivity to training data fluctuations).

Regularisation

Techniques that add constraints or penalties to a model to prevent overfitting and improve generalisation to new data.

Gradient Descent

An optimisation algorithm that iteratively adjusts parameters in the direction of steepest descent of the loss function.

Stochastic Gradient Descent

A variant of gradient descent that updates parameters using a randomly selected subset of training data each iteration.

Adam Optimiser

An adaptive learning rate optimisation algorithm combining momentum and RMSProp for efficient deep learning training.

Learning Rate

A hyperparameter that controls how much model parameters are adjusted with respect to the loss gradient during training.

Loss Function

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

Backpropagation

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

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

Density-Based Spatial Clustering of Applications with Noise — a clustering algorithm that finds arbitrarily shaped clusters based on density.

A method for discovering interesting relationships and patterns between variables in large datasets.

A machine learning paradigm where models learn from a continuous stream of data, accumulating knowledge over time without forgetting previously learned information.

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

A partitioning algorithm that divides data into k clusters by minimising the distance between points and their cluster centroids.

The application of deep neural networks to structured tabular datasets, competing with traditional methods like gradient boosting through specialised architectures and regularisation.

A situation where the distribution of classes in a dataset is significantly skewed, with some classes vastly outnumbering others.