Unit 3: Required

These datasets were chosen because they are familiar datasets from previous lessons and have binary targets for logistic regression.

After this lesson, students will be able to:

• Recall how to perform linear regression in scikit-learn.
• Demonstrate why logistic regression is a better alternative for classification than linear regression.
• Understand the concepts of probability, odds, e, log, and log-odds in relation to machine learning.
• Explain how logistic regression works.
• Interpret logistic regression coefficients.
• Use logistic regression with categorical features.
• Compare logistic regression with other models.
• Utilize different metrics for evaluating classifier models.
• Construct a confusion matrix based on predicted classes.

Before this lesson(s), students should already be able to:

• Load in and perform basic analysis and manipulation on data in Pandas
• Build and interpret a linear regression model
• Explain the basics of probability
• Distinguish between continuous and categorical variables
• Understand the bias-variance tradeoff and use confusion matrices to tune this balance

TOTAL (170 min)

• Refresher: Fitting and Visualizing a Linear Regression using scikit-learn (20 min)
• Refresher: Interpreting Linear Regression Coefficients (15 min)
• Predicting a Categorical Response (15 min)
• Using logistic regression for classification (10 min)
• Probability, odds ratio, e, log, and log-odds (30 min)
  • Understanding e and the natural logarithm (20 min)
  • The log-odds ratio (10 min)
• What is Logistic Regression? (10 min)
• Interpreting Logistic Regression Coefficients (20 min)
• Using Logistic Regression with Categorical Features (15 min)
• Comparing Logistic Regression to Other Models (10 min)
• Advanced Classifcation Metrics (25 min)
  • Accuracy, True Positive Rate, and False Negative Rate (15 min)
  • The accuracy paradox (10 min)
• OPTIONAL: How Many Samples Are Needed?
• Lesson Review

For more information on this topic, check out the following resources:

• Sklearn Logistic Regression Documentation
• Data School: Logistic Regression In-Depth
• Logistic Regression for Machine Learning
• Video: Andrew Ng on Logistic Regression