Machine learning toolbox for social scientists : applied predictive analytics with R /: applied predictive analytics with R. (2023)

• Record Type: Book
• Further Information: Note: Yigit Aydede.
• Authors: Aydede, Yigit
• Contents: 1. How We Define Machine Learning 2. Preliminaries Part 1 . Formal Look at Prediction 3. Bias-Variance Tradeoff 4. Overfitting Part 2 . Nonparametric Estimations 5. Parametric Estimations 6. Nonparametric Estimations - Basics 7. Smoothing 8. Nonparametric Classifier - kNN Part 3 . Self-learning 9. Hyperparameter Tuning 10. Tuning in Classification 11. Classification Example Part 4 . Tree-based Models 12. CART 13. Ensemble Learning 14. Ensemble Applications Part 5 . SVM & Neural Networks 15. Support Vector Machines 16. Artificial Neural Networks Part 6 . Penalized Regressions 17. Ridge 18. Lasso 19. Adaptive Lasso 20. Sparsity Part 7 . Time Series Forecasting 21. ARIMA models 22. Grid Search for Arima 23. Time Series Embedding 24. Random Forest with Times Series 25. Recurrent Neural Networks Part 8 . Dimension Reduction Methods 26. Eigenvectors and eigenvalues 27. Singular Value Decomposition 28. Rank r approximations 29. Moore-Penrose Inverse 30. Principle Component Analysis 31. Factor Analysis Part 9 . Network Analysis 32. Fundamentals 33. Regularized Covariance Matrix Part 10 . R Labs 34. R Lab 1 Basics 35. R Lab 2 Basics II 36. Simulations in R 37. Algorithmic Optimization 38. Imbalanced Data
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2023
• Extent: 1 online resource (580 pages), illustrations (black and white, and colour)
• Subjects: 300.285631; Social sciences -- Data processing; Social sciences -- Statistical methods; Machine learning; R (Computer program language)
• Languages: English
• ISBNs: 9781000958270
• Notes: Note: Description based on CIP data; resource not viewed.
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