leerdata

Leerdata, or training data, is the data used to train machine learning models. In supervised learning, leerdata consists of input features and the corresponding labels; in unsupervised learning, it may be unlabeled and used for discovering structure in data. Training data is typically divided into training, validation, and test sets to build, tune, and evaluate models.

Sources and formats: Training data may come from internal systems, sensors, user interactions, images, text, or synthetic data generated to augment real data. Formats vary from structured tabular data (CSV, Parquet) to unstructured data (images, audio, text).

Preparation: Data cleaning, normalization, feature extraction, and encoding are common steps. Data labeling is essential for supervised tasks and is often performed by humans or via crowdwork. Data provenance and versioning are important to track changes over time.

Ethics and privacy: Leerdata may include personal information; privacy-preserving techniques, consent, and compliance with laws (e.g., GDPR) are important. Bias and representativeness: unequal distribution can lead to biased models; mitigation includes balanced sampling, reweighting, or diverse data collection. Data quality and leakage: training data should not contain information that directly reveals the target (no data leakage).

Applications and challenges: Used across domains such as vision, natural language processing, speech, and tabular analytics. Challenges include data drift, scarcity of labeled data, labeling cost, and maintaining data quality. Techniques like data augmentation, transfer learning, and synthetic data help address these issues.