Generalisatiegap

Generalisatiegap refers to a phenomenon observed in machine learning where a model performs well on the data it was trained on but struggles to generalize to new, unseen data. This discrepancy highlights a fundamental challenge in building effective AI systems. The model has effectively "memorized" the training set rather than learning the underlying patterns and relationships that would allow it to make accurate predictions on novel examples.

Several factors can contribute to a generalisatiegap. One common cause is overfitting, where the model is too complex for the amount of training data available, leading it to capture noise and specific details of the training set that are not representative of the broader data distribution. Insufficient or biased training data can also lead to this gap, as the model may not have been exposed to a diverse enough range of examples to learn robust patterns.

Addressing the generalisatiegap involves various techniques. Data augmentation, which artificially increases the size and diversity of the training dataset, is a popular method. Regularization techniques, such as L1 and L2 regularization, penalize model complexity, discouraging overfitting. Cross-validation is another important tool, allowing developers to assess a model's performance on multiple subsets of the data, providing a more reliable estimate of its generalization ability. Careful feature selection and engineering can also help by focusing the model on the most relevant information.