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Machine Learning Tutorial

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

Confusion Matrix

A table that breaks down predictions into True Positive, False Positive, True Negative and False Negative counts.

  • True Positive (TP) — predicted positive, actually positive
  • False Positive (FP) — predicted positive, actually negative (Type I error)
  • True Negative (TN) — predicted negative, actually negative
  • False Negative (FN) — predicted negative, actually positive (Type II error)
Tip

Precision = TP/(TP+FP); Recall = TP/(TP+FN) — derive both directly from this matrix when asked.