Databricks Machine Learning Associate Question 325
Single answerYou are using hyperparameter tuning to optimize the performance of a machine learning model on Databricks. During the process, you notice that increasing the number of trials improves model accuracy initially but eventually leads to diminishing returns. What could explain this behavior?
- A
The model's performance reaches an upper bound due to the dataset's inherent patterns and noise.
- B
Increasing the number of trials always leads to continuous improvements in model accuracy.
- C
Too many trials can lead to overfitting of the validation data, reducing generalization.
- D
The number of trials does not affect model accuracy; only the choice of hyperparameters matters.
Show answer and explanation
Correct answer: A
Explanation
Increasing the number of trials during hyperparameter tuning allows the exploration of a broader range of hyperparameter combinations, which can lead to better model performance initially. However, as the number of trials increases, the improvements in accuracy will plateau due to the constraints of the dataset and the model's capacity. This explains why diminishing returns occur after a certain point.
- A. Correct.
This is correct because the model's accuracy is limited by the quality and complexity of the data, as well as the algorithm's capacity, leading to diminishing returns after a certain number of trials.
- B. Incorrect.
This is incorrect because while increasing trials can improve accuracy initially, there is a point where improvements stagnate due to the model's and data's limitations.
- C. Incorrect.
This is incorrect because overfitting typically depends on the selection of hyperparameters and how they are evaluated, not directly on the number of trials.
- D. Incorrect.
This is incorrect because the number of trials does affect model accuracy by exploring different hyperparameter combinations, though its impact diminishes over time.