Which of the following statements is false?
A) In machine learning, a model implements a machine-learning algorithm. In xe "machine learning:scikit-learn"xe "scikit-learn (sklearn) machine-learning library"scikit-learn, models are called estimators.
B) There are two parameter types in machine learning-those the estimator calculates as it learns from the data you provide and those you specify in advance when you create the scikit-learn estimator object that represents the model.
C) The machine-learning parameters the estimator calculates as it learns from the data are called hyperparameters-in the k-nearest neighbors algorithm, k is a hyperparameter.
D) For simplicity, we use scikit-learn's default hyperparameter values. In real-world machine-learning studies, you'll want to experiment with different values of k to produce the best possible models for your studies-this process is called hyperparameter tuning.

Question

Quizplus · Accepted Answer

- The statement in C is false because the parameters that the estimator calculates as it learns from the data are not called hyperparameters; they are called model parameters. Hyperparameters are the parameters that you specify in advance before the learning process begins. In the k-nearest neighbors algorithm, k is indeed a hyperparameter, but it is not calculated from the data; it is set by the user before the model is trained.

Which of the Following Statements Is False