regularisointivahvuuden

Regularisointivahvuuden, often translated as regularization strength or regularization parameter, is a hyperparameter used in machine learning and statistics to control the amount of regularization applied to a model. Regularization is a technique used to prevent overfitting, which occurs when a model learns the training data too well, including its noise and outliers, leading to poor performance on unseen data.

The regularization strength determines how much the model's complexity is penalized. A higher regularization strength imposes a stronger penalty on model complexity, encouraging simpler models. This can lead to underfitting if the strength is too high, meaning the model is too simplistic to capture the underlying patterns in the data. Conversely, a lower regularization strength imposes a weaker penalty, allowing for more complex models. If the strength is too low, the model may overfit.

Different regularization techniques, such as L1 (Lasso) and L2 (Ridge) regularization, use the regularization strength to modify the model's cost function. For L1 regularization, the penalty is proportional to the absolute value of the model's weights, promoting sparsity by driving some weights to exactly zero. For L2 regularization, the penalty is proportional to the square of the weights, shrinking weights towards zero but rarely setting them to exactly zero. The optimal value of regularisointivahvuuden is typically found through techniques like cross-validation, where different values are tested to determine which yields the best performance on a validation set.