Modelgegevens

Modelgegevens refers to the data used to build, train, and evaluate statistical or machine learning models. This data serves as the foundation upon which models learn patterns, relationships, and make predictions or classifications. The quality, relevance, and representativeness of modelgegevens are crucial for the performance and reliability of the resulting model.

There are typically two main types of modelgegevens: training data and test data. Training data is used to teach the model the underlying patterns in the data. This involves feeding the data to the model and allowing it to adjust its internal parameters to minimize errors or maximize accuracy. Test data, on the other hand, is held back and is not used during the training phase. It is used to evaluate how well the trained model generalizes to new, unseen data. This helps in assessing the model's predictive power and identifying issues like overfitting, where a model performs exceptionally well on training data but poorly on new data.

Modelgegevens can originate from various sources, including databases, sensor readings, user interactions, surveys, and publicly available datasets. Data preprocessing steps are often applied to modelgegevens before they are fed into a model. These steps can include cleaning missing values, transforming variables, scaling features, and encoding categorical data. The careful selection and preparation of modelgegevens are therefore fundamental to the successful development of any predictive or analytical model.