Feature Engineering for Machine Learning

Table of contents

1. Preface
   1. Introduction
   2. Conventions Used in This Book
   3. Using Code Examples
   4. O’Reilly Safari
   5. How to Contact Us
   6. Acknowledgments
      1. Special Thanks from Alice
      2. Special Thanks from Amanda
2. 1. The Machine Learning Pipeline
   1. Data
   2. Tasks
   3. Models
   4. Features
   5. Model Evaluation
3. 2. Fancy Tricks with Simple Numbers
   1. Scalars, Vectors, and Spaces
   2. Dealing with Counts
      1. Binarization
      2. Quantization or Binning
   3. Log Transformation
      1. Log Transform in Action
      2. Power Transforms: Generalization of the Log Transform
   4. Feature Scaling or Normalization
      1. Min-Max Scaling
      2. Standardization (Variance Scaling)
      3. ℓ2 Normalization
   5. Interaction Features
   6. Feature Selection
   7. Summary
   8. Bibliography
4. 3. Text Data: Flattening, Filtering, and Chunking
   1. Bag-of-X: Turning Natural Text into Flat Vectors
      1. Bag-of-Words
      2. Bag-of-n-Grams
   2. Filtering for Cleaner Features
      1. Stopwords
      2. Frequency-Based Filtering
      3. Stemming
   3. Atoms of Meaning: From Words to n-Grams to Phrases
      1. Parsing and Tokenization
      2. Collocation Extraction for Phrase Detection
   4. Summary
   5. Bibliography
5. 4. The Effects of Feature Scaling: From Bag-of-Words to Tf-Idf
   1. Tf-Idf : A Simple Twist on Bag-of-Words
   2. Putting It to the Test
      1. Creating a Classification Dataset
      2. Scaling Bag-of-Words with Tf-Idf Transformation
      3. Classification with Logistic Regression
      4. Tuning Logistic Regression with Regularization
   3. Deep Dive: What Is Happening?
   4. Summary
   5. Bibliography
6. 5. Categorical Variables: Counting Eggs in the Age of Robotic Chickens
   1. Encoding Categorical Variables
      1. One-Hot Encoding
      2. Dummy Coding
      3. Effect Coding
      4. Pros and Cons of Categorical Variable Encodings
   2. Dealing with Large Categorical Variables
      1. Feature Hashing
      2. Bin Counting
   3. Summary
   4. Bibliography
7. 6. Dimensionality Reduction: Squashing the Data Pancake with PCA
   1. Intuition
   2. Derivation
      1. Linear Projection
      2. Variance and Empirical Variance
      3. Principal Components: First Formulation
      4. Principal Components: Matrix-Vector Formulation
      5. General Solution of the Principal Components
      6. Transforming Features
      7. Implementing PCA
   3. PCA in Action
   4. Whitening and ZCA
   5. Considerations and Limitations of PCA
   6. Use Cases
   7. Summary
   8. Bibliography
8. 7. Nonlinear Featurization via K-Means Model Stacking
   1. k-Means Clustering
   2. Clustering as Surface Tiling
   3. k-Means Featurization for Classification
      1. Alternative Dense Featurization
   4. Pros, Cons, and Gotchas
   5. Summary
   6. Bibliography
9. 8. Automating the Featurizer: Image Feature Extraction and Deep Learning
   1. The Simplest Image Features (and Why They Don’t Work)
   2. Manual Feature Extraction: SIFT and HOG
      1. Image Gradients
      2. Gradient Orientation Histograms
      3. SIFT Architecture
   3. Learning Image Features with Deep Neural Networks
      1. Fully Connected Layers
      2. Convolutional Layers
      3. Rectified Linear Unit (ReLU) Transformation
      4. Response Normalization Layers
      5. Pooling Layers
      6. Structure of AlexNet
   4. Summary
   5. Bibliography
10. 9. Back to the Feature: Building an Academic Paper Recommender
    1. Item-Based Collaborative Filtering
    2. First Pass: Data Import, Cleaning, and Feature Parsing
       1. Academic Paper Recommender: Naive Approach
    3. Second Pass: More Engineering and a Smarter Model
       1. Academic Paper Recommender: Take 2
    4. Third Pass: More Features = More Information
       1. Academic Paper Recommender: Take 3
    5. Summary
    6. Bibliography
11. A. Linear Modeling and Linear Algebra Basics
    1. Overview of Linear Classification
    2. The Anatomy of a Matrix
       1. From Vectors to Subspaces
       2. Singular Value Decomposition (SVD)
       3. The Four Fundamental Subspaces of the Data Matrix
    3. Solving a Linear System
    4. Bibliography
12. Index