Relevance rate measures how well a piece of content, product, or recommendation matches what a user is looking for or needs. It is often calculated as the percentage of items shown that are considered relevant by users or meet specific criteria. A high relevance rate indicates that the system is successfully providing information or options…
Drift scores are numerical values that measure how much data has changed over time compared to a previous dataset. They help identify shifts or changes in the patterns, distributions, or characteristics of data. These scores are often used to monitor whether data used by a machine learning model is still similar to the data it…
Text simplification is the process of making written content easier to read and understand. This is done by using simpler words, shorter sentences, and clearer structure, while keeping the original meaning. It helps more people, including those with reading difficulties or those learning a new language, access information more easily.
Response filters are tools or processes that modify or manage the information sent back by a system after a request is made. They can check, change, or enhance responses before they reach the user or another system. This helps ensure that the output is correct, safe, and meets certain standards or requirements.
Output tracing is the process of following the results or outputs of a system, program, or process to understand how they were produced. It helps track the flow of information from input to output, making it easier to diagnose errors and understand system behaviour. By examining each step that leads to a final output, output…
Model performance frameworks are structured approaches used to assess how well a machine learning or statistical model is working. They help users measure, compare, and understand the accuracy, reliability, and usefulness of models against specific goals. These frameworks often include a set of metrics, testing methods, and evaluation procedures to ensure models perform as expected…
Neural feature analysis is the process of examining and understanding the patterns or characteristics that artificial neural networks use to make decisions. It involves identifying which parts of the input data, such as pixels in an image or words in a sentence, have the most influence on the network’s output. By analysing these features, researchers…
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…
Quantum model analysis is a way of using mathematical models based on quantum physics to understand and predict how very small particles, like atoms and electrons, behave. These models help scientists and engineers make sense of complex systems that classical physics cannot explain. By analysing quantum models, researchers can design new materials, medicines, and technology…
Neural layer analysis is the process of examining and understanding the roles and behaviours of individual layers within an artificial neural network. Each layer in a neural network transforms input data in specific ways, gradually extracting features or patterns that help the network make decisions. By analysing these layers, researchers and engineers can gain insights…