Answer: Hyperparameters are fixed before training, while model parameters are learned from data during training

## Explanation **Correct Answer: B** Hyperparameters and model parameters are fundamental concepts in machine learning with distinct roles: ### Hyperparameters: - **Set before training** begins - Define the **structure and behavior** of the model - Examples: learning rate, regularization coefficient, number of hidden layers, number of trees in a random forest - Not learned from data - must be specified by the data scientist - Significantly impact model performance and require tuning ### Model Parameters: - **Learned from data during training** - Represent the **internal coefficients or weights** of the model - Examples: weights in neural networks, coefficients in linear regression, split points in decision trees - Automatically adjusted during the training process to minimize the loss function - Used to make predictions on new data ### Why Other Options Are Incorrect: - **A**: Reverses the roles - hyperparameters are NOT learned from data - **C**: Model parameters are learned during training, not testing - **D**: Reverses the functions - hyperparameters specify structure, parameters make predictions This distinction is crucial for effective machine learning model development and optimization.

Author: Tanishq Prabhu