Module Learning Outcomes

Module Learning Outcomes

By the end of the module, students are expected to:

• Explain the general intuition behind linear models.
• Explain the fit and predict paradigm of linear models.
• Use scikit-learn’s LogisticRegression classifier.
  • Use fit, predict and predict_proba.
    
  • Use coef_ to interpret the model weights.
• Explain the advantages and limitations of linear classifiers.
• Apply scikit-learn regression model (e.g., Ridge) to regression problems.
• Relate the Ridge hyperparameter alpha to the LogisticRegression hyperparameter C.

Let’s start!