Out-of-Sample Bewertung refers to the process of assessing the performance of an künstliche Intelligenz (AI) model using data that was not included during the model’s training phase. This evaluation is crucial for understanding how well the model can generalize its learned patterns to new, unseen data, which is a key indicator of its effectiveness in real-world applications.
In KI und maschinellem Lernen, models are trained on a specific dataset, known as the training set. However, if we only evaluate the model on this training set, we may obtain an overly optimistic view of its performance. This is because the model may simply memorize the Trainingsdaten instead of learning to generalize. To combat this issue, out-of-sample evaluation is performed using a separate dataset, often called the test set or validation set, which contains data that the model has not encountered before.
Gängige Techniken für die Durchführung von Bewertungen außerhalb des Samples umfassen:
- Holdout-Methode: Splitting the entire dataset into a training set and a test set. The model is trained on the training set and evaluated on the test set.
- K-Fold-Kreuzvalidierung: Dividing the dataset into ‘k’ subsets. The model is trained ‘k’ times, each time using a different subset as the test set, while the remaining subsets are used for training. This method provides a more robust evaluation.
- Leave-One-Out-Kreuzvalidierung: A special case of k-fold cross-validation where ‘k’ is equal to the number of instances in the dataset. Each instance is used once as a test set while the remaining instances form the training set.
Insgesamt ist die Out-of-Sample-Bewertung ein grundlegender Schritt in der Modellentwicklung lifecycle, ensuring that the AI system is reliable and effective in practical scenarios.