parameteränderung

Parameteränderung, also known as parameter tuning or hyperparameter optimization, is a crucial process in the field of machine learning and data analysis. It involves adjusting the parameters of a model to improve its performance. Parameters can be categorized into two types: model parameters and hyperparameters.

Model parameters are learned from the data and are specific to the model. For example, in a linear regression model, the coefficients of the input features are model parameters. Hyperparameters, on the other hand, are set before the learning process begins and are not learned from the data. They control the behavior of the learning algorithm. Examples of hyperparameters include the learning rate in gradient descent algorithms, the number of trees in a random forest, and the regularization parameter in ridge regression.

The process of parameteränderung typically involves the following steps: 1) defining a range of values for each hyperparameter, 2) selecting a performance metric, 3) using a search strategy (such as grid search, random search, or more advanced methods like Bayesian optimization) to explore the hyperparameter space, and 4) evaluating the performance of each combination of hyperparameters using cross-validation.

The goal of parameteränderung is to find the combination of hyperparameters that results in the best performance according to the chosen metric. This process is essential for building effective and efficient machine learning models, as it allows for the optimization of the model's performance and generalization ability.