Unit 04 | Lazy Learning: Classification with k-NN |
| 9.0 hrs |
Upon completion of this module, you will be able to:
- explain the nearest neighbor algorithm for lazy classification
- apply k-NN to classification problems
- normalize data for better algorithm performance
- implement k-NN in R through code and through packages
- select and encode feature and perform feature engineering
Understanding Nearest Neighbor Classification
|
Required Work
Additional ResourcesSlide Deck & Data Sets |
Guest Lecture: k Nearest Neighbor Classification Algorithm
|
Required Work
Additional Resources |
Worked Example: k-NN with Iris Data Set in R
|
Required Work
Additional Resources
|
Worked Example I: Using kNN from the class Package
Work through the example presented in the text book in chapter 3 (pages 75 - 87). Implement the example code in R. The data set can be downloaded from the link below (as a CSV) or directly from the author's GitHub repository. Be sure to install the class package in your R environment before you work through the code.
Worked Example II: Using kNN from the caret Package
Work through the example presented in this tutorial using the Wine dataset. Be sure to install the caret package in your R environment before you work through the code. Here's some code you can use to download the data into R directly from the URL:
Code EditorCode Editor
|
Tutorial on the caret PackageWork through the tutorial to better understand the features of an important R package for machine learning. Be sure to install the caret package in your R environment before you work through the code.
|
Additional Resources
Data Sets
What it's like to be a Data Scientist
|
Required WorkView the interviews to get a bit of a sense what it's like to work as a data scientist. It's not always about math, and programming, and algorithms. Notice how much time they say is spent on shaping data and determining what the actual problem is that needs to be solved.
Additional Resources
|