Testaufteilung

Testaufteilung, also known as test splitting or test partitioning, is a technique used in software development and machine learning to divide a dataset into distinct subsets for the purpose of evaluating the performance of a model or system. The most common approach involves splitting the data into at least two sets: a training set and a testing set.

The training set is used to train the model, allowing it to learn patterns and relationships within the data. Once the model has been trained, its performance is then assessed using the testing set. This set is kept separate from the training data and is not used during the learning process. This ensures that the evaluation is performed on unseen data, providing a more realistic measure of how the model will perform in real-world scenarios.

In some cases, a third set, known as a validation set, is also employed. The validation set is used during the training phase to tune hyperparameters and prevent overfitting. Overfitting occurs when a model learns the training data too well, including its noise and specific characteristics, leading to poor performance on new data. The validation set helps to strike a balance between model complexity and generalization.

The specific ratios for splitting data into training, validation, and testing sets can vary depending on the project and the size of the dataset. Common splits include 70/30 (training/testing) or 60/20/20 (training/validation/testing). This methodical approach to data division is crucial for developing robust and reliable models.