Answer-first summary for fast verification
Answer: All of the above
All of the listed strategies are effective in reducing overfitting, each addressing the issue from a different angle. Early Stopping (Option C) prevents overfitting by stopping the training process early. Data Augmentation (Option A) increases the diversity of the training data, making the model more robust. Regularization (Option D) simplifies the model by penalizing large weights, and Dropout (Option B) randomly deactivates neurons to prevent the model from becoming too dependent on any single neuron. Combining these methods often yields the best results in preventing overfitting.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
Which of the following strategies can be used to reduce overfitting in machine learning models? Choose the best answer.
A
Data Augmentation (e.g., Image scaling, rotation to enhance training data diversity)
B
Dropout, a technique that randomly deactivates neurons during training to prevent overfitting
C
Early Stopping, halting training before the model starts overfitting
D
Regularization techniques like L1 and L2 to penalize large weights and simplify the model
E
All of the above
No comments yet.