You are previewing Machine Learning for Hackers.

Machine Learning for Hackers

Cover of Machine Learning for Hackers by Drew Conway... Published by O'Reilly Media, Inc.
  1. Machine Learning for Hackers
  2. Preface
    1. Machine Learning for Hackers
    2. How This Book Is Organized
    3. Conventions Used in This Book
    4. Using Code Examples
    5. Safari® Books Online
    6. How to Contact Us
    7. Acknowledgements
  3. 1. Using R
    1. R for Machine Learning
      1. Downloading and Installing R
      2. IDEs and Text Editors
      3. Loading and Installing R Packages
      4. R Basics for Machine Learning
      5. Further Reading on R
  4. 2. Data Exploration
    1. Exploration versus Confirmation
    2. What Is Data?
    3. Inferring the Types of Columns in Your Data
    4. Inferring Meaning
    5. Numeric Summaries
    6. Means, Medians, and Modes
    7. Quantiles
    8. Standard Deviations and Variances
    9. Exploratory Data Visualization
    10. Visualizing the Relationships Between Columns
  5. 3. Classification: Spam Filtering
    1. This or That: Binary Classification
    2. Moving Gently into Conditional Probability
    3. Writing Our First Bayesian Spam Classifier
      1. Defining the Classifier and Testing It with Hard Ham
      2. Testing the Classifier Against All Email Types
      3. Improving the Results
  6. 4. Ranking: Priority Inbox
    1. How Do You Sort Something When You Don’t Know the Order?
    2. Ordering Email Messages by Priority
      1. Priority Features of Email
    3. Writing a Priority Inbox
      1. Functions for Extracting the Feature Set
      2. Creating a Weighting Scheme for Ranking
      3. Weighting from Email Thread Activity
      4. Training and Testing the Ranker
  7. 5. Regression: Predicting Page Views
    1. Introducing Regression
      1. The Baseline Model
      2. Regression Using Dummy Variables
      3. Linear Regression in a Nutshell
    2. Predicting Web Traffic
    3. Defining Correlation
  8. 6. Regularization: Text Regression
    1. Nonlinear Relationships Between Columns: Beyond Straight Lines
      1. Introducing Polynomial Regression
    2. Methods for Preventing Overfitting
      1. Preventing Overfitting with Regularization
    3. Text Regression
      1. Logistic Regression to the Rescue
  9. 7. Optimization: Breaking Codes
    1. Introduction to Optimization
    2. Ridge Regression
    3. Code Breaking as Optimization
  10. 8. PCA: Building a Market Index
    1. Unsupervised Learning
  11. 9. MDS: Visually Exploring US Senator Similarity
    1. Clustering Based on Similarity
      1. A Brief Introduction to Distance Metrics and Multidirectional Scaling
    2. How Do US Senators Cluster?
      1. Analyzing US Senator Roll Call Data (101st–111th Congresses)
  12. 10. kNN: Recommendation Systems
    1. The k-Nearest Neighbors Algorithm
    2. R Package Installation Data
  13. 11. Analyzing Social Graphs
    1. Social Network Analysis
      1. Thinking Graphically
    2. Hacking Twitter Social Graph Data
      1. Working with the Google SocialGraph API
    3. Analyzing Twitter Networks
      1. Local Community Structure
      2. Visualizing the Clustered Twitter Network with Gephi
      3. Building Your Own “Who to Follow” Engine
  14. 12. Model Comparison
    1. SVMs: The Support Vector Machine
    2. Comparing Algorithms
  15. Works Cited
    1. Books
    2. Articles
  16. Index
  17. About the Authors
  18. Colophon
  19. Copyright
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Chapter 2. Data Exploration

Exploration versus Confirmation

Whenever you work with data, it’s helpful to imagine breaking up your analysis into two completely separate parts: exploration and confirmation. The distinction between exploratory data analysis and confirmatory data analysis comes down to us from the famous John Tukey,[6] who emphasized the importance of designing simple tools for practical data analysis. In Tukey’s mind, the exploratory steps in data analysis involve using summary tables and basic visualizations to search for hidden patterns in your data. In this chapter, we describe some of the basic tools that R provides for summarizing your data numerically, and then we teach you how to make sense of the results. After that, we show you some of the tools that exist in R for visualizing your data, and at the same time, we give you a whirlwind tour of the basic visual patterns that you should keep an eye out for in any gization.

But before you start searching through your first data set, we should warn you about a real danger that’s present whenever you explore data: you’re likely to find patterns that aren’t really there. The human mind is designed to find patterns in the world and will do so even when those patterns are just quirks of chance. You don’t need a degree in statistics to know that we human beings will easily find shapes in clouds after looking at them for only a few seconds. And plenty of people have convinced themselves that they’ve discovered hidden messages ...

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