Databricks Machine Learning Associate Question 327
Single answerYou are training a machine learning model using hyperparameter tuning in Databricks. During the tuning process, you notice that increasing the number of trials in your hyperparameter search slightly improves the model's accuracy but plateaus after a certain point. Which statement best explains the relationship between the number of trials and model accuracy in this scenario?
- A
Increasing the number of trials always results in higher model accuracy due to better hyperparameter exploration.
- B
Increasing the number of trials improves model accuracy up to a point, after which the returns diminish due to exploration-exploitation trade-offs.
- C
Model accuracy decreases as the number of trials increases because the search becomes too exhaustive and overfits the hyperparameters to the training data.
- D
There is no relationship between the number of trials and model accuracy since hyperparameter tuning results are completely random.
Show answer and explanation
Correct answer: B
Explanation
Increasing the number of trials in hyperparameter tuning generally leads to better exploration of the hyperparameter space, which can improve model accuracy. However, after a certain number of trials, the returns diminish as the process reaches the limits of optimization for the given dataset and model. This is due to the balance between exploring new hyperparameters and exploiting the best ones found so far, known as the exploration-exploitation trade-off.
- A. Incorrect.
This is incorrect because while increasing the number of trials can lead to better hyperparameter exploration, it does not guarantee continuous improvement in accuracy as diminishing returns occur after a certain number of trials.
- B. Correct.
This is correct because increasing the number of trials typically improves model accuracy up to a point, but the improvement plateaus due to the exploration-exploitation trade-off and inherent limitations in hyperparameter tuning.
- C. Incorrect.
This is incorrect because increasing the number of trials does not inherently decrease accuracy. Overfitting in hyperparameter tuning is managed through techniques like validation data splits.
- D. Incorrect.
This is incorrect because hyperparameter tuning is not random; it is a guided process using algorithms like grid search or Bayesian optimization, which are designed to optimize specific objectives.