Machine Learning Pocket Reference

Table of contents

1. Preface
   1. What to Expect
   2. Who This Book Is For
   3. Conventions Used in This Book
   4. Using Code Examples
   5. O’Reilly Online Learning
   6. How to Contact Us
   7. Acknowledgments
2. 1. Introduction
   1. Libraries Used
   2. Installation with Pip
   3. Installation with Conda
3. 2. Overview of the Machine Learning Process
4. 3. Classification Walkthrough: Titanic Dataset
   1. Project Layout Suggestion
   2. Imports
   3. Ask a Question
   4. Terms for Data
   5. Gather Data
   6. Clean Data
   7. Create Features
   8. Sample Data
   9. Impute Data
   10. Normalize Data
   11. Refactor
   12. Baseline Model
   13. Various Families
   14. Stacking
   15. Create Model
   16. Evaluate Model
   17. Optimize Model
   18. Confusion Matrix
   19. ROC Curve
   20. Learning Curve
   21. Deploy Model
5. 4. Missing Data
   1. Examining Missing Data
   2. Dropping Missing Data
   3. Imputing Data
   4. Adding Indicator Columns
6. 5. Cleaning Data
   1. Column Names
   2. Replacing Missing Values
7. 6. Exploring
   1. Data Size
   2. Summary Stats
   3. Histogram
   4. Scatter Plot
   5. Joint Plot
   6. Pair Grid
   7. Box and Violin Plots
   8. Comparing Two Ordinal Values
   9. Correlation
   10. RadViz
   11. Parallel Coordinates
8. 7. Preprocess Data
   1. Standardize
   2. Scale to Range
   3. Dummy Variables
   4. Label Encoder
   5. Frequency Encoding
   6. Pulling Categories from Strings
   7. Other Categorical Encoding
   8. Date Feature Engineering
   9. Add col_na Feature
   10. Manual Feature Engineering
9. 8. Feature Selection
   1. Collinear Columns
   2. Lasso Regression
   3. Recursive Feature Elimination
   4. Mutual Information
   5. Principal Component Analysis
   6. Feature Importance
10. 9. Imbalanced Classes
    1. Use a Different Metric
    2. Tree-based Algorithms and Ensembles
    3. Penalize Models
    4. Upsampling Minority
    5. Generate Minority Data
    6. Downsampling Majority
    7. Upsampling Then Downsampling
11. 10. Classification
    1. Logistic Regression
    2. Naive Bayes
    3. Support Vector Machine
    4. K-Nearest Neighbor
    5. Decision Tree
    6. Random Forest
    7. XGBoost
    8. Gradient Boosted with LightGBM
    9. TPOT
12. 11. Model Selection
    1. Validation Curve
    2. Learning Curve
13. 12. Metrics and Classification Evaluation
    1. Confusion Matrix
    2. Metrics
    3. Accuracy
    4. Recall
    5. Precision
    6. F1
    7. Classification Report
    8. ROC
    9. Precision-Recall Curve
    10. Cumulative Gains Plot
    11. Lift Curve
    12. Class Balance
    13. Class Prediction Error
    14. Discrimination Threshold
14. 13. Explaining Models
    1. Regression Coefficients
    2. Feature Importance
    3. LIME
    4. Tree Interpretation
    5. Partial Dependence Plots
    6. Surrogate Models
    7. Shapley
15. 14. Regression
    1. Baseline Model
    2. Linear Regression
    3. SVMs
    4. K-Nearest Neighbor
    5. Decision Tree
    6. Random Forest
    7. XGBoost Regression
    8. LightGBM Regression
16. 15. Metrics and Regression Evaluation
    1. Metrics
    2. Residuals Plot
    3. Heteroscedasticity
    4. Normal Residuals
    5. Prediction Error Plot
17. 16. Explaining Regression Models
    1. Shapley
18. 17. Dimensionality Reduction
    1. PCA
    2. UMAP
    3. t-SNE
    4. PHATE
19. 18. Clustering
    1. K-Means
    2. Agglomerative (Hierarchical) Clustering
    3. Understanding Clusters
20. 19. Pipelines
    1. Classification Pipeline
    2. Regression Pipeline
    3. PCA Pipeline
21. Index