Data Analytics : A Small Data Approach (Hardcover)

1. INTRODUCTION Who will benefit from this book Overview of a Data Analytics Pipeline Topics in a Nutshell 2. ABSTRACTION Regression & tree models Overview Regression Models Tree Models Remarks Exercises 3. RECOGNITION Logistic regression & ranking Overview Logistic Regression Model A Ranking Problem by Pairwise Comparison Statistical Process Control using Decision Tree Remarks Exercise 4. RESONANCE Bootstrap & random forests Overview How Bootstrap Works Random Forests Remarks Exercises 5. LEARNING (I) Cross validation & OOB Overview Cross-Validation Out-of-bag error in Random Forest Remarks Exercises 6. DIAGNOSIS Residuals & heterogeneity Overview Diagnosis in Regression Diagnosis in Random Forests Clustering Remarks Exercises 7. LEARNING (II) SVM & ensemble Learning Overview Support Vector Machine Ensemble Learning Remarks Exercises data analytics 8. SCALABILITY LASSO & PCA Overview LASSO Principal Component Analysis Remarks Exercises 9. PRAGMATISM Experience & experimental Overview Kernel Regression Model Conditional Variance Regression Model Remarks Exercises 10. SYNTHESIS Architecture & pipeline Overview Deep Learning inTrees Remarks Exercises CONCLUSION APPENDIX: A BRIEF REVIEW OF BACKGROUND KNOWLEDGE The normal distribution Matrix operations Optimization