Training data [Neurosurgery Wiki]

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====== Training data ======

To build a [[machine learning model]], you need a labeled [[dataset]] for [[training]]. This dataset consists of [[example]]s, where each data point is associated with a [[target]] or label. The model learns from this labeled data to make [[prediction]]s.
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To build a binary classification model, you need a labeled dataset for training. This dataset consists of examples where each data point is associated with a class label (either positive or negative). The model learns to distinguish between the two classes by identifying patterns and relationships in the training data.
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[[Neural network]]s and other [[artificial intelligence]] programs require an initial set of [[data]], called a training [[dataset]], to act as a [[baseline]] for further application and utilization. This [[dataset]] is the foundation for the program’s growing [[library]] of [[information]]. The [[training]] dataset must be accurately labeled before the model can process and learn from it.

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[[Training]] data is an extremely large [[dataset]] that is used to teach a [[machine learning]] model. Training data is used to teach prediction models that use machine learning algorithms how to extract features that are relevant to specific business goals. For supervised ML models, the training data is labeled.
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Simply put, training data is used to [[train]] an [[algorithm]]. Generally, training data is a certain percentage of an overall [[dataset]] along with a [[test set]]. As a rule, the better the [[training data]], the better the algorithm or classifier performs.

[[Big data]] and training data are not the same thing. Gartner calls big data “high-volume, high-velocity, and/or high-variety” and this information generally needs to be processed in some way for it to be truly useful.

Training data is [[labeled data]] used to teach AI models or machine learning algorithms.