Fundamentals of Machine Learning for Predictive Data Analytics · Algorithms, Worked Examples, and Case Studies by Kelleher, John D. -- Read -- Imperial Library of Trantor

Index

Structure of the Book How to Use this Book Online Resources Acknowledgements

Notation

Notational Conventions Notational Conventions for Probabilities

1 Machine Learning for Predictive Data Analytics

1.1 What Is Predictive Data Analytics? 1.2 What Is Machine Learning? 1.3 How Does Machine Learning Work? 1.4 What Can Go Wrong with Machine Learning? 1.5 The Predictive Data Analytics Project Lifecycle: CRISP-DM 1.6 Predictive Data Analytics Tools 1.7 The Road Ahead 1.8 Exercises

2 Data to Insights to Decisions

2.1 Converting Business Problems into Analytics Solutions

2.1.1 Case Study: Motor Insurance Fraud

2.2 Assessing Feasibility

2.2.1 Case Study: Motor Insurance Fraud

2.3 Designing the Analytics Base Table

2.3.1 Case Study: Motor Insurance Fraud

2.4 Designing and Implementing Features

2.4.1 Different Types of Data 2.4.2 Different Types of Features 2.4.3 Handling Time 2.4.4 Legal Issues 2.4.5 Implementing Features 2.4.6 Case Study: Motor Insurance Fraud

2.5 Summary 2.6 Further Reading 2.7 Exercises

3 Data Exploration

3.1 The Data Quality Report

3.1.1 Case Study: Motor Insurance Fraud

3.2 Getting to Know the Data

3.2.1 The Normal Distribution 3.2.2 Case Study: Motor Insurance Fraud

3.3 Identifying Data Quality Issues

3.3.1 Missing Values 3.3.2 Irregular Cardinality 3.3.3 Outliers 3.3.4 Case Study: Motor Insurance Fraud

3.4 Handling Data Quality Issues

3.4.1 Handling Missing Values 3.4.2 Handling Outliers 3.4.3 Case Study: Motor Insurance Fraud

3.5 Advanced Data Exploration

3.5.1 Visualizing Relationships Between Features 3.5.2 Measuring Covariance and Correlation

3.6 Data Preparation

3.6.1 Normalization 3.6.2 Binning 3.6.3 Sampling

3.7 Summary 3.8 Further Reading 3.9 Exercises

4 Information-based Learning

4.1 Big Idea 4.2 Fundamentals

4.2.1 Decision Trees 4.2.2 Shannon’s Entropy Model 4.2.3 Information Gain

4.3 Standard Approach: The ID3 Algorithm

4.3.1 A Worked Example: Predicting Vegetation Distributions

4.4 Extensions and Variations

4.4.1 Alternative Feature Selection and Impurity Metrics 4.4.2 Handling Continuous Descriptive Features 4.4.3 Predicting Continuous Targets 4.4.4 Tree Pruning 4.4.5 Model Ensembles

4.5 Summary 4.6 Further Reading 4.7 Exercises

5 Similarity-based Learning

5.1 Big Idea 5.2 Fundamentals

5.2.1 Feature Space 5.2.2 Measuring Similarity Using Distance Metrics

5.3 Standard Approach: The Nearest Neighbor Algorithm

5.3.1 A Worked Example

5.4 Extensions and Variations

5.4.1 Handling Noisy Data 5.4.2 Efficient Memory Search 5.4.3 Data Normalization 5.4.4 Predicting Continuous Targets 5.4.5 Other Measures of Similarity 5.4.6 Feature Selection

5.5 Summary 5.6 Further Reading 5.7 Epilogue 5.8 Exercises

6 Probability-based Learning

6.1 Big Idea 6.2 Fundamentals

6.2.1 Bayes’ Theorem 6.2.2 Bayesian Prediction 6.2.3 Conditional Independence and Factorization

6.3 Standard Approach: The Naive Bayes Model

6.3.1 A Worked Example

6.4 Extensions and Variations

6.4.1 Smoothing 6.4.2 Continuous Features: Probability Density Functions 6.4.3 Continuous Features: Binning 6.4.4 Bayesian Networks

6.5 Summary 6.6 Further Reading 6.7 Exercises

7 Error-based Learning

7.1 Big Idea 7.2 Fundamentals

7.2.1 Simple Linear Regression 7.2.2 Measuring Error 7.2.3 Error Surfaces

7.3 Standard Approach: Multivariable Linear Regression with Gradient Descent

7.3.1 Multivariable Linear Regression 7.3.2 Gradient Descent 7.3.3 Choosing Learning Rates and Initial Weights 7.3.4 A Worked Example

7.4 Extensions and Variations

7.4.1 Interpreting Multivariable Linear Regression Models 7.4.2 Setting the Learning Rate Using Weight Decay 7.4.3 Handling Categorical Descriptive Features 7.4.4 Handling Categorical Target Features: Logistic Regression 7.4.5 Modeling Non-linear Relationships 7.4.6 Multinomial Logistic Regression 7.4.7 Support Vector Machines

7.5 Summary 7.6 Further Reading 7.7 Exercises

8 Evaluation

8.1 Big Idea 8.2 Fundamentals 8.3 Standard Approach: Misclassification Rate on a Hold-out Test Set 8.4 Extensions and Variations

8.4.1 Designing Evaluation Experiments 8.4.2 Performance Measures: Categorical Targets 8.4.3 Performance Measures: Prediction Scores 8.4.4 Performance Measures: Multinomial Targets 8.4.5 Performance Measures: Continuous Targets 8.4.6 Evaluating Models after Deployment

8.5 Summary 8.6 Further Reading 8.7 Exercises

9 Case Study: Customer Churn

9.1 Business Understanding 9.2 Data Understanding 9.3 Data Preparation 9.4 Modeling 9.5 Evaluation 9.6 Deployment

10 Case Study: Galaxy Classification

10.1 Business Understanding

10.1.1 Situational Fluency

10.2 Data Understanding 10.3 Data Preparation 10.4 Modeling

10.4.1 Baseline Models 10.4.2 Feature Selection 10.4.3 The 5-level Model

10.5 Evaluation 10.6 Deployment

11 The Art of Machine Learning for Predictive Data Analytics

11.1 Different Perspectives on Prediction Models 11.2 Choosing a Machine Learning Approach

11.2.1 Matching Machine Learning Approaches to Projects 11.2.2 Matching Machine Learning Approaches to Data

11.3 Your Next Steps

A Descriptive Statistics and Data Visualization for Machine Learning

A.1 Descriptive Statistics for Continuous Features

A.1.1 Central Tendency A.1.2 Variation

A.2 Descriptive Statistics for Categorical Features A.3 Populations and Samples A.4 Data Visualization

A.4.1 Bar Plots A.4.2 Histograms A.4.3 Box Plots

B Introduction to Probability for Machine Learning

B.1 Probability Basics B.2 Probability Distributions and Summing Out B.3 Some Useful Probability Rules B.4 Summary

C Differentiation Techniques for Machine Learning

C.1 Derivatives of Continuous Functions C.2 The Chain Rule C.3 Partial Derivatives

Bibliography List of Figures List of Tables Index

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