Machine Learning Pocket Reference by Harrison, Matt -- Read -- Imperial Library of Trantor

Index

Preface

What to Expect Who This Book Is For Conventions Used in This Book Using Code Examples O’Reilly 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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