Logistic Regression

Main Idea: Find the parameters for a line that partitions a data set.

General Approach (MLIA: p. 84)

  1. Collect Data: any method
  2. Prepare Data: Convert to numeric data if needed.
  3. Analyze: Any method.
  4. Train: Find the optimal coefficients to classify the data.
  5. Use: Given new data, classify it based on the previously classified data.

Pros, Cons, and Data Types

  • Computationally Cheap
  • Easy to implement
  • Easy to interpret
  • Succeptible to overfitting
  • Not always accurate
Data Types:
  • Numeric Values
  • Nominal Values