Description:

  • To classify the data into 1 of possible catergories

Boolean classification:

  • We turn the outcome to which denotes positive and negative
  • Then round up/down to the nearest -1 or 1
  • We can then have :
    • True positive,
    • True negative,
    • False positive, and Type 1 Error
      • False positive rate = false positive / total real negative
    • False negative, and Type 2 Error
      • false negative rate = false negative / total real positive
    • Error rate is defined by (false positive + false negative)/ total observations

Least Square Classification

Multi-class Classification

Model-based Classification


Nearest Neighbor for Classification

Binary classification: