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Answer: Coefficients of a linear regression model
Hyperparameters are configuration settings used to structure the learning process of a machine learning algorithm. They are set before the learning process begins and are not derived from the data. Examples include the learning rate (C), regularization parameter (D), number of trees in a random forest (E), and number of hidden layers in a neural network (A). These are specified before training and influence the algorithm's behavior. On the other hand, the coefficients of a linear regression model (B) are parameters learned from the data during training, not hyperparameters. They represent the weights assigned to features and are determined by fitting the model to the training data.
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Which of the following is NOT considered a hyperparameter in a machine learning algorithm? Choose only ONE best answer.
A
Number of hidden layers in a neural network
B
Coefficients of a linear regression model
C
Learning rate
D
Regularization parameter
E
Number of trees in a random forest