träningstestfördelning
Träningstestfördelning, often translated as training-test split, is a fundamental technique in machine learning used to evaluate the performance of a model. It involves dividing a dataset into two distinct subsets: a training set and a testing set. The training set is used to "teach" the machine learning model by allowing it to learn patterns and relationships within the data. The testing set, on the other hand, is held back and used to assess how well the trained model generalizes to unseen data.
The purpose of this split is to prevent overfitting. Overfitting occurs when a model learns the training
The ratio of the split can vary, but common configurations include 70/30, 80/20, or even 90/10 for