overoptimisation

Overoptimisation refers to the process of tuning a system, algorithm, or model to such a degree that its performance on past data or specific training examples is significantly improved, but its ability to generalize to new, unseen data is compromised. This phenomenon is particularly common in machine learning and statistical modeling, where algorithms are trained on datasets to learn patterns and make predictions. When an overoptimised model is presented with data that differs even slightly from its training set, its performance can degrade substantially.

This issue arises because the model has essentially memorized the training data, including its noise and specific idiosyncrasies, rather than learning the underlying general principles. Techniques such as overfitting, where a model is too complex for the amount of data available, can lead to overoptimisation. The goal in model development is typically to find a balance where the model performs well on both training and validation/test sets, indicating good generalisation.

Common strategies to combat overoptimisation include employing cross-validation, using regularization techniques, increasing the size or diversity of the training dataset, and simplifying the model architecture. By carefully monitoring performance on data not used during the training phase, developers can identify and mitigate overoptimisation, ensuring that their models are robust and reliable in real-world applications.