hüperparameetreid

Hyperparameters are parameters whose values are set before the learning process begins. They are not learned from the data but are rather external configurations that control the behavior of a machine learning algorithm. Unlike model parameters, which are learned during training, hyperparameters are chosen by the data scientist or engineer.

The choice of hyperparameters can significantly impact the performance of a machine learning model. For instance, in a neural network, hyperparameters include the learning rate, the number of hidden layers, and the number of neurons in each layer. In a support vector machine, hyperparameters might include the regularization parameter (C) and the kernel type.

Finding optimal hyperparameters is a crucial step in model development. This process is often referred to as hyperparameter tuning or hyperparameter optimization. Common methods for hyperparameter tuning include grid search, random search, and more advanced techniques like Bayesian optimization. Grid search exhaustively tries all possible combinations of specified hyperparameter values. Random search explores a random subset of the hyperparameter space, which can be more efficient for high-dimensional spaces. Bayesian optimization uses probabilistic models to intelligently select hyperparameters that are most likely to yield good performance.

The goal of hyperparameter tuning is to find a set of hyperparameters that result in a model with good generalization performance on unseen data, avoiding overfitting or underfitting. This is typically evaluated using a validation set separate from the training and testing datasets.