Yleistämisongelmat

Yleistämisongelmat, also known as generalization problems, is a concept encountered in machine learning and statistics. It refers to the difficulty a model faces in performing well on new, unseen data after being trained on a specific dataset. Essentially, the model has learned the training data too well, including its noise and specific idiosyncrasies, and as a result, it fails to generalize to the broader population or underlying pattern.

There are two primary forms of generalization problems: overfitting and underfitting. Overfitting occurs when a model is too complex for the amount of training data available. It captures noise and random fluctuations in the training set, leading to high accuracy on the training data but poor performance on new data. Conversely, underfitting happens when a model is too simple to capture the underlying structure of the data. It fails to learn the important patterns in the training data, resulting in poor performance on both training and new data.

Addressing yleistämisongelmat is crucial for building effective machine learning models. Techniques such as cross-validation, regularization, and gathering more diverse training data are commonly employed to mitigate these issues. The goal is to find a model that strikes a balance between fitting the training data sufficiently and being able to generalize to unseen examples.