trainingset

Trainingset (also known as training data or training set) is the portion of data used to train a machine learning or statistical model. In supervised learning, a trainingset consists of input features and corresponding target labels; in unsupervised learning, it may consist mainly of input samples used to learn structure or representation.

Key properties include representativeness and coverage of the domain, distribution alignment with real-world data, class balance, size, and labeling quality. Poorly curated trainingset can introduce bias or cause overfitting.

Datasets are often partitioned into trainingset, validation set, and test set. The trainingset is used to optimize model parameters; the validation set helps tune hyperparameters and monitor learning progress; the test set provides an unbiased assessment of final performance. In k-fold cross-validation, multiple training subsets are created from the overall data.

Common preprocessing steps include cleaning, handling missing values, normalization or scaling, encoding categorical variables, feature engineering, and data augmentation for images or text. It is important to maintain separation so that information from the test set does not leak into the trainingset.

Size and selection considerations: larger and more diverse trainingsets generally improve model performance but require more compute and storage. Data provenance, licensing, and privacy considerations apply, and some domains use synthetic or augmented trainingsets to supplement real data.