Table of Contents
[ ii ]
Managing data with R 39
Saving, loading, and removing R data structures 39
Importing and saving data from CSV les 41
Exploring and understanding data 42
Exploring the structure of data 43
Exploring numeric variables 44
Measuring the central tendency – mean and median 45
Measuring spread – quartiles and the ve-number summary 47
Visualizing numeric variables – boxplots 49
Visualizing numeric variables – histograms 51
Understanding numeric data – uniform and normal distributions 53
Measuring spread – variance and standard deviation 54
Exploring categorical variables 56
Measuring the central tendency – the mode 58
Exploring relationships between variables 59
Visualizing relationships – scatterplots 59
Examining relationships – two-way cross-tabulations 61
Summary 64
Chapter 3: Lazy Learning – Classication Using
Nearest Neighbors 65
Understanding nearest neighbor classication 66
The k-NN algorithm 66
Measuring similarity with distance 69
Choosing an appropriate k 70
Preparing data for use with k-NN 72
Why is the k-NN algorithm lazy? 74
Example – diagnosing breast cancer with the k-NN algorithm 75
Step 1 – collecting data 76
Step 2 – exploring and preparing the data 77
Transformation – normalizing numeric data 79
Data preparation – creating training and test datasets 80
Step 3 – training a model on the data 81
Step 4 – evaluating model performance 83
Step 5 – improving model performance 84
Transformation – z-score standardization 85
Testing alternative values of k 86
Summary 87
Chapter 4: Probabilistic Learning – Classication
Using Naive Bayes 89
Understanding Naive Bayes 90
Basic concepts of Bayesian methods 90
Understanding probability 91
Understanding joint probability 92
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