Category: Data Science

Model Calibration Metrics

Model calibration metrics are tools used to measure how well a machine learning model’s predicted probabilities reflect actual outcomes. They help determine if the model’s confidence in its predictions matches real-world results. Good calibration means when a model predicts something with 80 percent certainty, it actually happens about 80 percent of the time.

Graph Knowledge Analysis

Graph knowledge analysis is the process of examining and understanding data that is organised as networks or graphs, where items are represented as nodes and their relationships as edges. This approach helps identify patterns, connections and insights that might not be obvious from traditional data tables. It is commonly used to study complex systems, such…

Quantum Data Efficiency

Quantum data efficiency refers to how effectively quantum computers use data to solve problems or perform calculations. It measures how much quantum information is needed to achieve a certain level of accuracy or result, often compared with traditional computers. By using less data or fewer resources, quantum systems can potentially solve complex problems faster or…

Graph-Based Extraction

Graph-based extraction is a method for finding and organising information by representing data as a network of interconnected points, or nodes, and links between them. This approach helps to identify relationships and patterns that might not be obvious in plain text or tables. It is commonly used in areas like text analysis and knowledge management…

AI-Driven Forecasting

AI-driven forecasting uses artificial intelligence to predict future events based on patterns found in historical data. It automates the process of analysing large amounts of information and identifies trends that might not be visible to humans. This approach helps organisations make informed decisions by providing more accurate and timely predictions.

Quantum State Analysis

Quantum state analysis is the process of examining and understanding the condition or configuration of a quantum system, such as an atom or a photon. It involves measuring and interpreting the various possible states that the system can be in, often using mathematical tools and experiments. This analysis helps scientists predict how the quantum system…

Graph-Based Modeling

Graph-based modelling is a way of representing data, objects, or systems using graphs. In this approach, items are shown as points, called nodes, and the connections or relationships between them are shown as lines, called edges. This method helps to visualise and analyse complex networks and relationships in a clear and structured way. Graph-based modelling…

Graph Predictive Systems

Graph predictive systems are tools or models that use the structure of graphs, which are networks of connected points, to make predictions or forecasts. These systems analyse the relationships and connections between items, such as people, places, or things, to predict future events or behaviours. They are often used when data is naturally structured as…

Model Inference Metrics

Model inference metrics are measurements used to evaluate how well a machine learning model performs when making predictions on new data. These metrics help determine if the model is accurate, fast, and reliable enough for practical use. Common metrics include accuracy, precision, recall, latency, and throughput, each offering insight into different aspects of the model’s…