hiperoparaméter

Hiperparaméter is a term used in machine learning to describe parameters whose values are set before the learning process begins. Unlike model parameters, which are learned from the data during training, hyperparameters are external configurations that control the learning algorithm's behavior. Examples of hyperparameters include the learning rate in gradient descent, the number of trees in a random forest, or the regularization strength in logistic regression.

The selection of appropriate hyperparameters is crucial for achieving optimal model performance. Poorly chosen hyperparameters can lead to underfitting, where the model fails to capture the underlying patterns in the data, or overfitting, where the model learns the training data too well and performs poorly on unseen data. The process of finding the best set of hyperparameters is known as hyperparameter tuning or optimization.

Common hyperparameter tuning techniques include grid search, random search, and Bayesian optimization. Grid search exhaustively searches through a predefined grid of hyperparameter values. Random search randomly samples hyperparameter combinations from a specified distribution. Bayesian optimization uses a probabilistic model to guide the search for optimal hyperparameters, often leading to more efficient exploration of the hyperparameter space.

The goal of hyperparameter tuning is to find a set of hyperparameters that results in a model that generalizes well to new, unseen data. This often involves a trade-off between model complexity and performance, and the optimal choice can depend on the specific dataset and the problem being addressed.