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AN INTRODUCTION TO MACHINE LEARNING

M I C H A E L C L A R KC E N T E R F O R S O C I A L R E S E A R C HU N I V E R S I T Y O F N O T R E D A M EA N I N T R O D U C T I O N T O M A C H I N E L E A R N I N GW I T H A P P L I C AT I O N S I N RMachine Learning2 ContentsPreface5 INTRODUCTION : Explanation & Prediction6 Some Terminology7 Tools You Already Have7 The Standard Linear Model7 Logistic Regression8 Expansions of Those Tools9 Generalized Linear Models9 Generalized Additive Models9 The Loss Function10 Continuous Outcomes10 Squared Error10 Absolute Error10 Negative Log-likelihood10R Example11 Categorical Outcomes11 Misclassification11 Binomial log-likelihood11 Exponential12 Hinge Loss12 Regularization12R Example133 Applications in RBias-Variance Tradeoff14 Bias & Variance14 The Tradeoff15 Diagnosing Bias-Variance Issues & Possible Solutions16 Worst Case Scenario16 High Variance16 High Bias16 Cross-Validation16 Adding Another Validation Set17K-fold Cross-Validation17 Leave-one-out Cross-Validation17 Bootstrap18 Other Stuff18 Model Assessment & Selection18 Beyond Classification Accuracy.

Machine Learning 6 Introduction: Explanation & Prediction FOR ANY PARTICULAR ANALYSIS CONDUCTED, emphasis can be placed on understanding the underlying mechanisms which have spe-cific theoretical underpinnings, versus a focus that dwells more on

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