Grid search is a method of parameter adjustment.

The correct answer is B. FALSE. Option B is correct because grid search is a method of hyperparameter tuning, not parameter adjustment - these are two distinct concepts in machine learning. Parameters (such as weights and biases in a neural network) are learned automatically during the training process via…

Machine Learning Basics

Question

Options

ATRUE
BFALSE

How the community answered

(32 responses)

A
16% (5)
B
84% (27)

Explanation

Option B is correct because grid search is a method of hyperparameter tuning, not parameter adjustment - these are two distinct concepts in machine learning. Parameters (such as weights and biases in a neural network) are learned automatically during the training process via optimization algorithms like gradient descent. Hyperparameters (such as learning rate, tree depth, or regularization strength) are set before training and cannot be learned from data - grid search exhaustively evaluates a predefined grid of hyperparameter combinations to find the best-performing configuration.

Option A is wrong because it conflates "parameters" with "hyperparameters," which is a common but critical mix-up.

Memory tip: Think of it this way - parameters are found by the model, while hyperparameters are found for the model. Grid search belongs to the "for the model" category, making it a hyperparameter search strategy, not a parameter adjustment method.

Topics

#grid search#hyperparameter tuning#parameter optimization#model training

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