treningsdatasett

A treningsdatasett, or training dataset, is a collection of data used to train a machine learning model. This dataset typically consists of examples, where each example includes input features and a corresponding output or target value. The model learns patterns, relationships, and rules from this data to make predictions or decisions on new, unseen data. The quality and quantity of the training dataset significantly impact the performance and accuracy of the machine learning model. A larger and more representative dataset generally leads to a more robust and reliable model. Data preprocessing steps, such as cleaning, normalization, and feature engineering, are often applied to the training dataset to improve its suitability for the model. The process of training involves iteratively adjusting the model's parameters based on the errors it makes when processing the training data. It is crucial that the training data accurately reflects the real-world scenarios the model will encounter to avoid issues like overfitting, where the model performs well on the training data but poorly on new data.