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Machine Learning Tutorial

Supervised learning, classification, neural networks and core ML algorithms.

Bias-Variance Tradeoff

Bias is error from overly simplistic assumptions; variance is error from being too sensitive to the training data. Total error = bias² + variance + irreducible noise.

  • High bias → underfitting — the model is too rigid to capture real patterns
  • High variance → overfitting — the model captures noise, not just signal
  • More model complexity generally lowers bias but raises variance, and vice versa