Advanced Predictive Classification Models

This week’s topics

Advanced Predictive Classification Models

1. Decision trees and ensemble methods
2. Hyperparameter tuning
3. Evaluating predictive performance using ROC-AUC, precision, and recall
4. Week 06: Lecture 29: Predictive Analytics: Classification Tree Model
5. Week 06: Lecture 30: Predictive Analytics: Building Classification Tree Model using Python
6. Precision, Recall, F1 score, True Positive|Deep Learning Tutorial 19 (Tensorflow2.0, Keras & Python)

What you will be able to do

• 1.2 Critically evaluate the use of data analytic methods.
• 3.3 Apply an appropriate programming language or tool to demonstrate how predictive analytic techniques are used in forecasting future events.

Activity

Start with this week’s 3 videos, then: Seminar, guided reading and a short written task: critically evaluate the use of data analytic methods.

Study materials

Videos

• Precision, Recall, F1 score, True Positive|Deep Learning Tutorial 19 (Tensorflow2.0, Keras & Python) · YouTube · codebasics · 12 min · for registered students
• Decision and Classification Trees, Clearly Explained!!! · YouTube · StatQuest with Josh Starmer · 18 min · for registered students
• Classification Trees in Python from Start to Finish · YouTube · StatQuest with Josh Starmer · 66 min · for registered students

Further resources

• MIT 15.071 The Analytics Edge (Spring 2017) · Reading · MIT OpenCourseWare, course · for registered students
• Principles of Data Science, ch. 6: Decision-Making Using Machine Learning Basics · PDF · OpenStax, textbook chapter; pp. 275–340 · for registered students