Core ML Concepts · intermediate · concept 22 of 187

🧠Bias-Variance Tradeoff

Bias: error from overly simplistic assumptions (underfitting). Variance: error from sensitivity to training data fluctuations (overfitting). The tradeoff is fundamental, reducing one often increases the other. Every architecture also carries an inductive bias, assumptions baked into its structure, like CNNs assuming nearby pixels matter most.