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In the context of machine learning, hyperparameters are parameters –like settings for the algorithm– whose values are set before the learning process begins. These parameters are not learned from the data but are set externally and remain constant throughout the training process. They control the learning process and affect the behavior and performance of a machine learning algorithm.
Hyperparameters are not the same as model parameters, which are the values or weights that the algorithm learns from the training data. Model parameters are optimized during the training process to minimize the error or loss function.
Examples of hyperparameters in machine learning include:

Learning Rate: A hyperparameter used in optimization algorithms (like Gradient Descent) that determines the size of the steps taken during parameter updates. It controls the speed at which a model learns.

Number of Hidden Layers and Neurons in Neural Networks: Hyperparameters that define the architecture of a neural network. The number of layers, neurons per layer, activation functions, etc., are set before training.

Regularization Strength: Hyperparameters in models (like Lasso or Ridge regression) that control the penalty applied to the coefficients to prevent overfitting.

Kernel Type and Parameters in Support Vector Machines (SVM): Hyperparameters that define the type of kernel function and their associated parameters.

Number of Trees and Depth in Random Forests: Hyperparameters that define the number of decision trees and their maximum depth in a Random Forest algorithm.

Batch Size: Hyperparameter used in training deep learning models that defines the number of samples processed before updating the model's weights.
Tuning hyperparameters is a crucial step in building machine learning models. The selection of appropriate hyperparameter values can significantly impact the model's performance, accuracy, and generalization to new data. Techniques such as grid search, random search, and more advanced methods like Bayesian optimization or genetic algorithms are used to find the optimal combination of hyperparameter values that lead to the best model performance.