Answer / Bhagwat Patel

There is often a trade-off between model accuracy and interpretability. Complex models like deep neural networks can achieve high accuracy but may be difficult to interpret due to their many layers and parameters. On the other hand, simpler models like logistic regression are easier to understand but typically have lower accuracy. Striking a balance between model complexity and interpretability is essential for ensuring both good performance and transparency.

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