Imbalanced Classification with Python: Better Metrics, Balance Skewed Classes, Cost-Sensitive Learning

Copyright
Contents
Preface
I Introduction
II Foundation
	What is Imbalanced Classification
		Tutorial Overview
		Classification Predictive Modeling
		Imbalanced Classification Problems
		Causes of Class Imbalance
		Challenge of Imbalanced Classification
		Examples of Imbalanced Classification
		Further Reading
		Summary
	Intuition for Imbalanced Classification
		Tutorial Overview
		Create and Plot a Binary Classification Problem
		Create Synthetic Dataset with a Class Distribution
		Effect of Skewed Class Distributions
		Further Reading
		Summary
	Challenge of Imbalanced Classification
		Tutorial Overview
		Why Imbalanced Classification Is Hard
		Compounding Effect of Dataset Size
		Compounding Effect of Label Noise
		Compounding Effect of Data Distribution
		Further Reading
		Summary
III Model Evaluation
	Tour of Model Evaluation Metrics
		Tutorial Overview
		Challenge of Evaluation Metrics
		Taxonomy of Classifier Evaluation Metrics
		How to Choose an Evaluation Metric
		Further Reading
		Summary
	The Failure of Accuracy
		Tutorial Overview
		What Is Classification Accuracy?
		Accuracy Fails for Imbalanced Classification
		Example of Accuracy for Imbalanced Classification
		Further Reading
		Summary
	Precision, Recall, and F-measure
		Tutorial Overview
		Precision Measure
		Recall Measure
		Precision vs. Recall
		F-measure
		Further Reading
		Summary
	ROC Curves and Precision-Recall Curves
		Tutorial Overview
		ROC Curves and ROC AUC
		Precision-Recall Curves and AUC
		ROC and PR Curves With a Severe Imbalance
		Further Reading
		Summary
	Probability Scoring Methods
		Tutorial Overview
		Probability Metrics
		Log Loss Score
		Brier Score
		Further Reading
		Summary
	Cross-Validation for Imbalanced Datasets
		Tutorial Overview
		Challenge of Evaluating Classifiers
		Failure of k-Fold Cross-Validation
		Fix Cross-Validation for Imbalanced Classification
		Further Reading
		Summary
IV Data Sampling
	Tour of Data Sampling Methods
		Tutorial Overview
		Problem of an Imbalanced Class Distribution
		Balance the Class Distribution With Sampling
		Tour of Popular Data Sampling Methods
		Further Reading
		Summary
	Random Data Sampling
		Tutorial Overview
		Random Sampling
		Random Oversampling
		Random Undersampling
		Further Reading
		Summary
	Oversampling Methods
		Tutorial Overview
		Synthetic Minority Oversampling Technique
		SMOTE for Balancing Data
		SMOTE for Classification
		SMOTE With Selective Sample Generation
		Further Reading
		Summary
	Undersampling Methods
		Tutorial Overview
		Undersampling for Imbalanced Classification
		Methods that Select Examples to Keep
		Methods that Select Examples to Delete
		Combinations of Keep and Delete Methods
		Further Reading
		Summary
	Oversampling and Undersampling
		Tutorial Overview
		Binary Test Problem and Decision Tree Model
		Manually Combine Data Sampling Methods
		Standard Combined Data Sampling Methods
		Further Reading
		Summary
V Cost-Sensitive
	Cost-Sensitive Learning
		Tutorial Overview
		Not All Classification Errors Are Equal
		Cost-Sensitive Learning
		Cost-Sensitive Imbalanced Classification
		Cost-Sensitive Methods
		Further Reading
		Summary
	Cost-Sensitive Logistic Regression
		Tutorial Overview
		Imbalanced Classification Dataset
		Logistic Regression for Imbalanced Classification
		Weighted Logistic Regression with Scikit-Learn
		Grid Search Weighted Logistic Regression
		Further Reading
		Summary
	Cost-Sensitive Decision Trees
		Tutorial Overview
		Imbalanced Classification Dataset
		Decision Trees for Imbalanced Classification
		Weighted Decision Tree With Scikit-Learn
		Grid Search Weighted Decision Tree
		Further Reading
		Summary
	Cost-Sensitive Support Vector Machines
		Tutorial Overview
		Imbalanced Classification Dataset
		SVM for Imbalanced Classification
		Weighted SVM With Scikit-Learn
		Grid Search Weighted SVM
		Further Reading
		Summary
	Cost-Sensitive Deep Learning in Keras
		Tutorial Overview
		Imbalanced Classification Dataset
		Neural Network Model in Keras
		Deep Learning for Imbalanced Classification
		Weighted Neural Network With Keras
		Further Reading
		Summary
	Cost-Sensitive Gradient Boosting with XGBoost
		Tutorial Overview
		Imbalanced Classification Dataset
		XGBoost Model for Classification
		Weighted XGBoost for Class Imbalance
		Tune the Class Weighting Hyperparameter
		Further Reading
		Summary
VI Advanced Algorithms
	Probability Threshold Moving
		Tutorial Overview
		Converting Probabilities to Class Labels
		Threshold-Moving for Imbalanced Classification
		Optimal Threshold for ROC Curve
		Optimal Threshold for Precision-Recall Curve
		Optimal Threshold Tuning
		Further Reading
		Summary
	Probability Calibration
		Tutorial Overview
		Problem of Uncalibrated Probabilities
		How to Calibrate Probabilities
		SVM With Calibrated Probabilities
		Decision Tree With Calibrated Probabilities
		Grid Search Probability Calibration With KNN
		Further Reading
		Summary
	Ensemble Algorithms
		Tutorial Overview
		Bagging for Imbalanced Classification
		Random Forest for Imbalanced Classification
		Easy Ensemble for Imbalanced Classification
		Further Reading
		Summary
	One-Class Classification
		Tutorial Overview
		One-Class Classification for Imbalanced Data
		One-Class Support Vector Machines
		Isolation Forest
		Minimum Covariance Determinant
		Local Outlier Factor
		Further Reading
		Summary
VII Projects
	Framework for Imbalanced Classification Projects
		Tutorial Overview
		What Algorithm To Use?
		Use a Systematic Framework
		Detailed Framework for Imbalanced Classification
		Further Reading
		Summary
	Project: Haberman Breast Cancer Classification
		Tutorial Overview
		Haberman Breast Cancer Survival Dataset
		Explore the Dataset
		Model Test and Baseline Result
		Evaluate Probabilistic Models
		Make Prediction on New Data
		Further Reading
		Summary
	Project: Oil Spill Classification
		Tutorial Overview
		Oil Spill Dataset
		Explore the Dataset
		Model Test and Baseline Result
		Evaluate Models
		Make Prediction on New Data
		Further Reading
		Summary
	Project: German Credit Classification
		Tutorial Overview
		German Credit Dataset
		Explore the Dataset
		Model Test and Baseline Result
		Evaluate Models
		Make Prediction on New Data
		Further Reading
		Summary
	Project: Microcalcification Classification
		Tutorial Overview
		Mammography Dataset
		Explore the Dataset
		Model Test and Baseline Result
		Evaluate Models
		Make Predictions on New Data
		Further Reading
		Summary
	Project: Phoneme Classification
		Tutorial Overview
		Phoneme Dataset
		Explore the Dataset
		Model Test and Baseline Result
		Evaluate Models
		Make Prediction on New Data
		Further Reading
		Summary
VIII Appendix
	Getting Help
		Imbalanced Classification Books
		Machine Learning Books
		Python APIs
		Ask Questions About Imbalanced Classification
		How to Ask Questions
		Contact the Author
	How to Setup Python on Your Workstation
		Tutorial Overview
		Download Anaconda
		Install Anaconda
		Start and Update Anaconda
		Install the Imbalanced-Learn Library
		Install the Deep Learning Libraries
		Install the XGBoost Library
		Further Reading
		Summary
IX Conclusions
	How Far You Have Come