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

Chapter 5. Regression: Predicting Page Views

Introducing Regression

In the abstract, regression is a very simple concept: you want to predict one set of numbers given another set of numbers. For example, actuaries might want to predict how long a person will live given their smoking habits, while meteorologists might want to predict the next day’s temperature given the previous day’s temperature. In general, we’ll call the numbers you’re given inputs and the numbers you want to predict outputs. You’ll also sometimes hear people refer to the inputs as predictors or features.

What makes regression different from classification is that the outputs are really numbers. In classification problems like those we described in Chapter 3, you might use numbers as a dummy code for a categorical distinction so that 0 represents ham and 1 represents spam. But these numbers are just symbols; we’re not exploiting the “numberness” of 0 or 1 when we use dummy variables. In regression, the essential fact about the outputs is that they really are numbers: you want to predict things like temperatures, which could be 50 degrees or 71 degrees. Because you’re predicting numbers, you want to be able to make strong statements about the relationship between the inputs and the outputs: you might want to say, for example, that when the number of packs of cigarettes a person smokes per day doubles, their predicted life span gets cut in half.

The problem, of course, is that wanting to make precise numerical predictions ...

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