Custom inputs are user interface elements that allow people to enter information or make choices in a way that is different from standard text boxes, checkboxes, or radio buttons. They are designed to fit specific needs or improve the way users interact with a website or app. Custom inputs can include things like sliders for…
Input shape refers to the specific dimensions or structure of data that a computer model, such as a neural network, expects to receive. This includes the number of features, rows, columns, or channels in the data. Correctly matching the input shape is essential for the model to process the information accurately and avoid errors. It…
Model flags are settings or parameters that control the behaviour, features, or performance of a machine learning model. They can enable or disable certain functions, adjust how the model processes data, or set thresholds for predictions. Model flags help developers and users customise models to fit specific needs or environments.
A Model Chooser is a tool or system that helps users select the most appropriate machine learning or statistical model for a specific task or dataset. It considers factors like data type, problem requirements, and performance goals to suggest suitable models. Model Choosers can be manual guides, automated software, or interactive interfaces that streamline the…
Keyword Boost is a strategy used in digital marketing and search engine optimisation to increase the visibility of specific words or phrases within online content. By focusing on these targeted keywords, websites can attract more visitors searching for related topics. This can involve adjusting website text, blog posts, or advertisements to feature the chosen keywords…
Regression sets are collections of test cases used to check that recent changes in software have not caused any existing features or functions to stop working as expected. They help ensure that updates, bug fixes, or new features do not introduce new errors into previously working areas. These sets are usually run automatically and are…
Label errors occur when the information assigned to data, such as categories or values, is incorrect or misleading. This often happens during data annotation, where mistakes can result from human error, misunderstanding, or unclear guidelines. Such errors can negatively impact the performance and reliability of machine learning models trained on the data.
Accuracy drops refer to a noticeable decrease in how well a system or model makes correct predictions or outputs. This can happen suddenly or gradually, and often signals that something has changed in the data, environment, or the way the system is being used. Identifying and understanding accuracy drops is important for maintaining reliable performance…
Model benchmarks are standard tests or sets of tasks used to measure and compare the performance of different machine learning models. These benchmarks provide a common ground for evaluating how well models handle specific challenges, such as recognising images, understanding language, or making predictions. By using the same tests, researchers and developers can objectively assess…
A/B variants are two different versions of something, such as a webpage, email, or advertisement, created to test which version performs better. Each version is shown to a different group of users, and their reactions or behaviours are measured and compared. This approach helps organisations make decisions based on real data rather than assumptions.