Answer / Rekha Prajapati

Accuracy and interpretability often present a trade-off in machine learning: n1. Complex Models vs. Simplicity: More complex models tend to be more accurate but less interpretable, while simpler models may be easier to understand but less powerful.n2. Overfitting vs. Underfitting: Finding the right balance between fitting the model too closely to training data (overfitting) and not capturing enough information (underfitting) can be challenging.n3. Explainability vs. Performance: Improving a model's explainability may come at the cost of its performance, and vice versa.

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