AI Dictionary › AI Fundamentals

Dataset

A dataset is an organized collection of data used to train, validate or test a machine learning model. It can be a table with rows and columns, a collection of labeled images, an archive of texts, or any other structured collection of examples an algorithm can learn from. It is the starting point of any machine learning project: without data, there is nothing to learn from.

Definition

How it works

A dataset typically consists of observations, the rows or individual examples, and features, the columns or variables describing each observation. In supervised learning problems it also includes a label or target, the value the model must learn to predict. A dataset's quality, completeness, correctness, representativeness relative to the real cases the model will encounter, often matters more than the sophistication of the algorithm chosen to use it.

Applications

Every phase of machine learning requires datasets with different roles: the training set teaches the model, the validation set helps choose the best hyperparameters, the test set measures final performance on data never seen during development. Public datasets such as ImageNet for computer vision, or shared text benchmarks, have accelerated research progress for years, allowing different models to be fairly compared on the same data.

History & etymology

The word comes from joining data and set, and it appears in the computing vocabulary as early as the 1960s, when early processing systems began handling structured collections of information on tapes and punch cards. With the growth of statistical machine learning from the 1980s and 90s onward, the term dataset became specialized to specifically mean the collection of examples used to train and evaluate a model.

How it's used in Grace

Every Grace professional scenario is built on an implicit dataset of real cases analyzed throughout the learning path: the 520 scenarios cover diverse professional situations exactly as a well-built dataset covers the variety of real cases.

Related terms

• Train-test split
• Training Data
• Cross-validation
• Data cleaning
• Missing values

More in AI Fundamentals

• LLM (Large Language Model)
• Token
• Context Window
• Temperature
• AI Hallucination
• Fine-tuning
• RAG (Retrieval Augmented Generation)
• Embedding

Put it into practice

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