4.1 Summary

This week we will introduce our second machine learning task: classification. After introducing the task, we will see how to re-use methods we have already learned to perform the task. This week, we will focus on on nonparametric classification techniques, in particular KNN and decision trees.

• Keywords: Classification, Bayes Classifier, Bayes Error, Nonparametric Classification, k-Nearest Neighbors, Decision Trees, Misclassification Rate, Accuracy

4.2 Learning Objectives

After completing this week, you are expected to be able to:

• Differentiate between regression and classification tasks.
• Estimate and calculate conditional probabilities.
• Understand how conditional probabilities relate to classifications.
• Use R packages and functions to fit KNN and decision tree models and make classification or estimate conditional probabilities.
• Calculate classification metrics such as accuracy and misclassification rate.
• Select models by manipulating their flexibility through the use of a tuning parameter.
• Avoid overfitting by selecting an a model of appropriate flexibility through the use of a validation set.

4.3 Reading

4.4 Video

4.5 Assignments

4.6 Office Hours

4.7 Additional Information

• koaning.io: High on Probability, Low on Confidence
• koaning.io: The Future of Data Science is Past
• Statistical Thinking: Classification vs. Prediction