Answer / Tanya

In machine learning models, the trade-off between bias and variance represents a fundamental challenge in model design. Bias refers to the error that results from an incorrect model assumption or simplification, while variance describes the error caused by the model's sensitivity to fluctuations in the training data. A high-bias model (underfitting) may fail to capture essential patterns in the data and will result in poor generalization to unseen data. Conversely, a high-variance model (overfitting) may capture noise or irrelevant information from the training data, resulting in poor performance on new data. The goal is to find a balance between bias and variance to create an accurate model.

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