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 Learning2ContentsPreface5Introduction: Explanation & Prediction6Some Terminology7Tools You Already Have7The Standard Linear Model7Logistic Regression8Expansions of Those Tools9Generalized Linear Models9Generalized Additive Models9The Loss Function10Continuous Outcomes10Squared Error10Absolute Error10Negative Log-likelihood10R Example11Categorical Outcomes11Misclassification11Binomial log-likelihood11Exponential12Hinge Loss12Regularization12R Example133Applications in RBias-Variance Tradeoff14Bias & Variance14The Tradeoff15Diagnosing Bias-Variance Issues & Possible Solutions16Worst Case Scenario16High Variance16High Bias16Cross-Validation16Adding Another Validation Set17K-fold Cross-Validation17Leave-one-out Cross-Validation17Bootstrap18Other Stuff18Model Assessment & Selection18Beyond Classification Accuracy.
to statistical or machine learning (ML) techniques for those that might ... Regarding programming, one ... is all that is required to get started with machine learning. The Standard Linear Model All introductory statistics courses will cover linear regression in great
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