Regularisaatiotermejä

Regularisaatiotermejä are additional components added to the loss function in machine learning models. Their primary purpose is to prevent overfitting, a phenomenon where a model learns the training data too well, including its noise and outliers, leading to poor performance on unseen data. By penalizing complex models, regularisation terms encourage simpler solutions that generalize better.

The most common types of regularisation terms are L1 and L2 regularisation. L2 regularisation, also known as weight decay, adds a penalty proportional to the square of the magnitude of the model's weights. This tends to shrink the weights towards zero but rarely makes them exactly zero. L1 regularisation, on the other hand, adds a penalty proportional to the absolute value of the weights. This encourages sparsity in the model, meaning it can drive some weights to exactly zero, effectively performing feature selection by ignoring less important features.

The strength of the regularisation is controlled by a hyperparameter, often denoted by lambda (λ). A higher lambda value imposes a stronger penalty and leads to a more regularized model, while a lower lambda value results in less regularization. Choosing the appropriate regularisation strength is crucial and is typically done through techniques like cross-validation.

Other forms of regularisation exist, such as elastic net regularisation, which combines both L1 and L2 penalties. Dropout is another popular technique, particularly in neural networks, where during training, randomly selected neurons are ignored, preventing co-adaptation of neurons and promoting robustness. Ultimately, the goal of all regularisaatiotermejä is to improve the model's ability to generalize to new, unseen data.