Chapter 2 Statistical Learning

Learning objectives:

• Understand Vocabulary for prediction
• Understand “Error”/Accuracy
• Understand Parametric vs Nonparametric Models
• Describe the trade-off between more accurate models and more interpretable models.
• Compare and contrast supervised and unsupervised learning.
• Compare and contrast regression and classification problems.
• Measure the accuracy/goodness of regression model fits.
• Measure the accuracy/goodness of classification model fits.
• Describe how bias and variance contribute to the model error.
• Understand overfitting.
• Recognize KNN.
• Understand the role of tuning in ML models.