hyperparametreilla

Hyperparametreilla refers to the hyperparameters of a machine learning model. These are 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 define the model's architecture or control the learning process itself.

Examples of hyperparameters include the learning rate in gradient descent, the number of layers in a neural network, the depth of a decision tree, or the regularization strength. Choosing appropriate hyperparameters is crucial for the performance of a machine learning model. If hyperparameters are not set well, the model may underfit the data (fail to capture the underlying patterns) or overfit the data (learn the training data too well, leading to poor generalization on new, unseen data).

The process of finding the optimal set of hyperparameters is called hyperparameter tuning or hyperparameter optimization. Common methods for hyperparameter tuning include grid search, random search, and Bayesian optimization. These methods involve training and evaluating the model multiple times with different hyperparameter combinations to identify the set that yields the best performance on a validation dataset. The final model is then trained on the entire training dataset using these optimized hyperparameters.