Lasso Regression

Feature Engineering

The process of using domain knowledge to create, select, and transform input variables to improve model performance.

Data Augmentation

Techniques that artificially increase the size and diversity of training data through transformations like rotation, flipping, and cropping.

Class Imbalance

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

SMOTE

Synthetic Minority Over-sampling Technique — a method for addressing class imbalance by generating synthetic examples of the minority class.

Label Noise

Errors or inconsistencies in the annotations of training data that can degrade model performance and lead to unreliable predictions if not properly addressed.

A machine learning paradigm where agents learn optimal behaviour through trial and error, receiving rewards or penalties.

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

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

A training strategy that presents examples to a model in a meaningful order, typically from easy to hard.

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

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

A versioned catalogue of trained machine learning models with metadata, lineage, and approval workflows, enabling reproducible deployment and governance at enterprise scale.

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