2. Polynomial and Logistic Regression

Syllabus Points Covered

Software automation

Algorithms in machine learning

• Investigate common applications of key ML algorithms

  • data analysis and forecasting

• Describe types of algorithms associated with ML

  • logistic regression

Programming for automation

• Design, develop and apply ML regression models using an OOP to predict numeric values

  • polynomial regression

  • logistic regression

Chapter Contents

• 2.1. Polynomial and Logistic Regression
• 2.2. Polynomial Regression
  • 2.2.1. Glossary
• 2.3. The Relationship Between Linear Regression and Polynomial Regression
• 2.4. Building a Polynomial Regression Model
• 2.5. Extension: Selecting The Polynomial Degree
• 2.6. Logistic Regression
  • 2.6.1. Glossary
• 2.7. Measuring Error
• 2.8. Building a Logistic Regression Model
• 2.9. Predicting With A Logistic Regression Model
  • 2.9.1. Class Prediction
  • 2.9.2. Class Probability
• 2.10. Extension: Further Classification Metrics
  • 2.10.1. Confusion Matrices
  • 2.10.2. Precision, Recall and F1 Score
  • 2.10.3. Probability Threshold
  • 2.10.4. Glossary
• 2.11. Extension: Multiple Logistic Regression