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Answer: Increase Model Complexity
**Correct Answer: Increase Model Complexity.** When a model underfits, it's too simplistic to grasp the data's nuances. Enhancing the model's complexity, such as opting for a more sophisticated algorithm or adding layers/neurons in neural networks within Databricks MLlib, can help. Conversely, reducing complexity or adjusting regularization levels are strategies better suited for overfitting scenarios, where the model is overly complex and may capture noise instead of the actual data patterns.
Author: LeetQuiz Editorial Team
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Your machine learning model is not capturing the underlying patterns in the training data effectively, suggesting it's too simple. Which strategy, supported by Databricks MLlib, would you use to tackle this underfitting issue?
A
Decrease Regularization
B
Increase Model Complexity
C
Decrease Model Complexity
D
Increase Regularization
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