Table conversion is the process of changing data from one table format to another. This can involve switching between file types, rearranging columns or rows, or adapting the structure for a different software application. Table conversion helps ensure that data remains usable and consistent when moved between systems or tools.
Metadata enrichment is the process of adding extra information to existing data to make it more useful and meaningful. This can include details like keywords, descriptions, categories or links to related content. Enriched metadata helps people and systems find, understand and use the data more easily.
Output labels are the names or categories that a system or model assigns to its results. In machine learning or data processing, these labels represent the possible answers or outcomes that a model can predict. They help users understand what each result means and make sense of the data produced.
Dataset merge is the process of combining two or more separate data collections into a single, unified dataset. This helps bring together related information from different sources, making it easier to analyse and gain insights. Merging datasets typically involves matching records using one or more common fields, such as IDs or names.
Spreadsheet hooks are tools or features that let you run certain actions automatically when something changes in a spreadsheet, such as editing a cell or adding a new row. They are often used to trigger scripts, send notifications, or update information in real time. Hooks help automate repetitive tasks and keep data up to date…
Behaviour mapping is a method used to observe and record how people interact with a particular environment or space. It involves tracking where, when, and how certain actions or behaviours occur, often using diagrams or maps. This approach helps identify patterns and understand how spaces are actually used, which can inform improvements or changes.
Staging models are frameworks that describe how a process, condition, or disease progresses through different phases or stages over time. They help to organise information, predict outcomes, and guide decisions by breaking down complex progressions into understandable steps. These models are commonly used in medicine, psychology, education, and project management to track changes and plan…
Feature ranking is the process of ordering the input variables of a dataset by their importance or relevance to a specific outcome or prediction. It helps identify which features have the most influence on the results of a model, allowing data scientists to focus on the most significant factors. This technique can make models simpler,…
Overfitting checks are methods used to ensure that a machine learning model is not just memorising the training data but can also make accurate predictions on new, unseen data. Overfitting happens when a model learns too much detail or noise from the training set, which reduces its ability to generalise. By performing checks, developers can…
Data sampling is the process of selecting a smaller group from a larger set of data to analyse or make predictions. This helps save time and resources because it is often not practical to work with every single piece of data. By carefully choosing a representative sample, you can still gain useful insights about the…