Chapter 5

Further Explorations in Classification

This chapter examines several other algorithms for classification including kNN and naïve Bayes. We look at the power of adding more data.

Evaluating classifiers: training sets and test data
10-fold cross validation
Which is better: adding more data or improving the algorithm?
the kNN algorithm
Python implementation of kNN
Naïve Bayes
Naïve Bayes Example: The Microsoft Shopping Cart and health monitors
using numerical attributes with naïve Bayes

The PDF for chapter 5

Python Code

Data

2 Comments to Chapter 5

by Gary

On September 11, 2010 at 11:37 pm

Chapter 5 pages are numbered with 4-#s vs 5-#
by Amy Sams

On October 25, 2010 at 11:55 pm

Page 4 (at the bottom): insert “instead” –> “In this chapter[,]our algorithm is not outputing a number[;instead,]it’s outputing….”
Page 5: Move “Python Implementation of kNN” and rest to next page (more uniform)
Page 6 (at bottom, 1st paragraph not code): insert “inches” –> So here, item[N]ame is ….weight as 5 foot and 3 [inches] and …”
Page 11 (5th paragraph): misspelled probability –> “With the nearest neighbor algorithms, it is…..based on [probability]…”
Page 11 (6th paragraph): combine 1st & 2nd sentences –> “Nearest Neighbor approaches are called lazy learners [because when we .....]”
Page 12 (1st paragraph): misspelled probability –> “…and have you tell me the [probability] of that person…”
Page 16 (2nd paragraph): capitalization –> “We know that only …. in the U.S. [h]ave this form of cancer.”
Page 19 (1st paragraph, 3rd sentence): add “ly” –> “If stores can effective[ly] remind us of what ….”
Page 34 (Before Challenge Heading): misspelled independent and remove to –> “What we are going to do is just [] assume that they are independent!”

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A Programmer's Guide to Data Mining