What is the Bias-Variance Tradeoff? How do you diagnose which one your model suffers from?

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The Bias-Variance Tradeoff describes the tension between two sources of error in a model. Bias — error from oversimplifying the problem. Model can't capture the underlying pattern. Symptom: high training error AND high validation error (underfitting). Variance — error from being too sensitive to the training data . Model captures noise. Symptom: low training error BUT high validation error (overfitting). Total error = Bias² + Variance + Irreducible Noise . Reducing one often increases the other. How to diagnose: • High training error → high bias (underfitting). Fix: bigger model, more features, less regularization. • Big gap between training and validation → high variance (overfitting). Fix: more data, regularization, simpler model, dropout. This is the #1 most-asked ML interview question. Don't just define bias and variance — also explain how to *diagnose*: high training error = bias; low training but high validation error = variance. Naming the symptom and the fix is what separates a good answer.