Financial Risk Manager Part 1
Get started today
Ultimate access to all questions.
Which of the following statements best describe the difference between hyperparameters and model parameters?
Exam-Like
Community
TTanishq
A
Hyperparameters are learned from data during training, while model parameters are fixed before training
B
Hyperparameters are fixed before training, while model parameters are learned from data during training
C
Hyperparameters are fixed before training, while model parameters are learned from data during testing
D
Hyperparameters are used to make predictions, while model parameters are used to specify the structure of the model
Explanation:
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.
Comments
Loading comments...