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Applications in Supervised Learning
Classification

PPOL 670 | Introduction to Data Science

Lecture Materials for Week 11

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Learning Objectives

In the Asynchronous Lecture

• Delve into performance metrics for classification problems.
• Explore logistic regression as a way of fiting linear model to binary data.
• Discuss KNN and decision trees for classication.
• Learn about support vector machines.

In the Synchronous Lecture

• Explore how to implement the models discussed this week using caret.

If you have any questions while watching the pre-recorded material, be sure to write them down and to bring them up during the synchronous portion of the lecture.

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Asynchronous Materials

The following tabs contain pre-recorded lecture materials for class this week. Please review these materials prior to the synchronous lecture.

Total time: Approx. 46 Minutes

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Classification

Relevant Slides

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Logistic Regression

Relevant Slides

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KNN/DT for Classification

Relevant Slides

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Support Vector Machines

Relevant Slides

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The following materials were generated for students enrolled in PPOL670. Please do not distribute without permission.

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