Machine Learning Pocket Reference. Working with Structured Data in Python

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• Preface
  • What to Expect
  • Who This Book Is For
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Introduction
  • Libraries Used
  • Installation with Pip
  • Installation with Conda
• 2. Overview of the Machine Learning Process
• 3. Classification Walkthrough: Titanic Dataset
  • Project Layout Suggestion
  • Imports
  • Ask a Question
  • Terms for Data
  • Gather Data
  • Clean Data
  • Create Features
  • Sample Data
  • Impute Data
  • Normalize Data
  • Refactor
  • Baseline Model
  • Various Families
  • Stacking
  • Create Model
  • Evaluate Model
  • Optimize Model
  • Confusion Matrix
  • ROC Curve
  • Learning Curve
  • Deploy Model
• 4. Missing Data
  • Examining Missing Data
  • Dropping Missing Data
  • Imputing Data
  • Adding Indicator Columns
• 5. Cleaning Data
  • Column Names
  • Replacing Missing Values
• 6. Exploring
  • Data Size
  • Summary Stats
  • Histogram
  • Scatter Plot
  • Joint Plot
  • Pair Grid
  • Box and Violin Plots
  • Comparing Two Ordinal Values
  • Correlation
  • RadViz
  • Parallel Coordinates
• 7. Preprocess Data
  • Standardize
  • Scale to Range
  • Dummy Variables
  • Label Encoder
  • Frequency Encoding
  • Pulling Categories from Strings
  • Other Categorical Encoding
  • Date Feature Engineering
  • Add col_na Feature
  • Manual Feature Engineering
• 8. Feature Selection
  • Collinear Columns
  • Lasso Regression
  • Recursive Feature Elimination
  • Mutual Information
  • Principal Component Analysis
  • Feature Importance
• 9. Imbalanced Classes
  • Use a Different Metric
  • Tree-based Algorithms and Ensembles
  • Penalize Models
  • Upsampling Minority
  • Generate Minority Data
  • Downsampling Majority
  • Upsampling Then Downsampling
• 10. Classification
  • Logistic Regression
  • Naive Bayes
  • Support Vector Machine
  • K-Nearest Neighbor
  • Decision Tree
  • Random Forest
  • XGBoost
  • Gradient Boosted with LightGBM
  • TPOT
• 11. Model Selection
  • Validation Curve
  • Learning Curve
• 12. Metrics and Classification Evaluation
  • Confusion Matrix
  • Metrics
  • Accuracy
  • Recall
  • Precision
  • F1
  • Classification Report
  • ROC
  • Precision-Recall Curve
  • Cumulative Gains Plot
  • Lift Curve
  • Class Balance
  • Class Prediction Error
  • Discrimination Threshold
• 13. Explaining Models
  • Regression Coefficients
  • Feature Importance
  • LIME
  • Tree Interpretation
  • Partial Dependence Plots
  • Surrogate Models
  • Shapley
• 14. Regression
  • Baseline Model
  • Linear Regression
  • SVMs
  • K-Nearest Neighbor
  • Decision Tree
  • Random Forest
  • XGBoost Regression
  • LightGBM Regression
• 15. Metrics and Regression Evaluation
  • Metrics
  • Residuals Plot
  • Heteroscedasticity
  • Normal Residuals
  • Prediction Error Plot
• 16. Explaining Regression Models
  • Shapley
• 17. Dimensionality Reduction
  • PCA
  • UMAP
  • t-SNE
  • PHATE
• 18. Clustering
  • K-Means
  • Agglomerative (Hierarchical) Clustering
  • Understanding Clusters
• 19. Pipelines
  • Classification Pipeline
  • Regression Pipeline
  • PCA Pipeline
• Index

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