Gradient Boosting Machines are a type of machine learning model that combines many simple decision trees to create a more accurate and powerful prediction system. Each tree tries to correct the mistakes made by the previous ones, gradually improving the model’s performance. This method is widely used for tasks like predicting numbers or sorting items…
Category: Data Science
Spectral Clustering
Spectral clustering is a method used to group data points into clusters based on how closely they are connected to each other. It works by representing the data as a graph, where each point is a node and edges show how similar points are. The technique uses mathematics from linear algebra, specifically eigenvalues and eigenvectors,…
Normalizing Flows
Normalising flows are mathematical methods used to transform simple probability distributions into more complex ones. They do this by applying a series of reversible steps, making it possible to model complicated data patterns while still being able to calculate probabilities exactly. This approach is especially useful in machine learning for tasks that require both flexible…
Causal Inference
Causal inference is the process of figuring out whether one thing actually causes another, rather than just being linked or happening together. It helps researchers and decision-makers understand if a change in one factor will lead to a change in another. Unlike simple observation, causal inference tries to rule out other explanations or coincidences, aiming…
Domain Adaptation
Domain adaptation is a technique in machine learning where a model trained on data from one environment or context is adjusted to work well in a different but related environment. This is useful when collecting labelled data for every new situation is difficult or expensive. Domain adaptation methods help models handle changes in data, such…
Cognitive Load Balancing
Cognitive load balancing is the process of managing and distributing mental effort to prevent overload and improve understanding. It involves organising information or tasks so that people can process them more easily and efficiently. Reducing cognitive load helps learners and workers focus on what matters most, making it easier to remember and use information.
Anomaly Detection
Anomaly detection is a technique used to identify data points or patterns that do not fit the expected behaviour within a dataset. It helps to spot unusual events or errors by comparing new information against what is considered normal. This process is important for finding mistakes, fraud, or changes that need attention in a range…
Feature Engineering
Feature engineering is the process of transforming raw data into meaningful inputs that improve the performance of machine learning models. It involves selecting, modifying, or creating new variables, known as features, that help algorithms understand patterns in the data. Good feature engineering can make a significant difference in how well a model predicts outcomes or…
Model Drift
Model drift happens when a machine learning model’s performance worsens over time because the data it sees changes from what it was trained on. This can mean the model makes more mistakes or becomes unreliable. Detecting and fixing model drift is important to keep predictions accurate and useful.
Data Labelling
Data labelling is the process of adding meaningful tags or labels to raw data so that machines can understand and learn from it. This often involves identifying objects in images, transcribing spoken words, or marking text with categories. Labels help computers recognise patterns and make decisions based on the data provided.