PPT-The Bias-Variance Trade-Off

Oliver Schulte Machine Learning 726 Estimating Generalization Error Presentation Title At Venue The basic problem Once Ive built a classifier how accurate will it be on future test data Problem of Induction Its hard to make predictions especially about the future Yogi Berra. berkeleyedu Statistics Department University of California Berkeley CA 94720 ABSTRACT Recent work has shown that combining multiple versions of unstable classifiers such as trees or neural nets results in reduced test set error To study this the conc Hinkins and H. Lock Oh, IRS, and Fritz Scheuren, Ernst & Young 1122 South 5th Ave., Bozeman, MT 59715 Key Words : Repeated Samples ; Permanent (1996) describe the SO1 corporate Random Numbers sample, Boosting, Bagging, Random Forests and More. Yisong Yue. Supervised Learning. Goal:. learn predictor h(x) . High accuracy (low error). Using training data {(x. 1. ,y. 1. ),…,(. x. n. ,y. n. )}. Person. 1 Rich Maclin Bias-Variance Decomposition for RegressionBias-Variance Analysis of Learning AlgorithmsEnsemble MethodsEffect of Bagging on Bias and Variance Example: 20 pointsy = x + 2 sin(1.5x) + N(0, Text Classification 2. David . Kauchak. cs459. Fall . 2012. adapted from:. http://www.stanford.edu/class/cs276/handouts/. lecture10-textcat-naivebayes.ppt. http://www.stanford.edu/class/cs276/handouts/lecture11-vector-classify.ppt. Andrew . Mullhaupt. Portfolio Selection. Maximize profit with risk bound:. In ‘unit risk’ coordinates:. Mean-variance portfolio. THE END. Transaction Costs. Commissions and Fees. Taxes. Slippage. Fig.1.Baggingperformancewithforwardstepwisefeatureselection.Theallfeatureslineshowsperformanceofbaggingwithall200features.separatingpointbetweenrelevantandirrelevantfeatures.Withtoomanyvari-ables,thei Selection bias Information bias Confounding bias Bias is an error in an epidemiologic study that results in an incorrect estimation of the association between exposure and outcome. Is present when th Winter 2012. Daniel Weld. Slides adapted from Tom . Dietterich. , Luke Zettlemoyer, Carlos . Guestrin. , . Nick Kushmerick, Padraig Cunningham. © Daniel S. Weld. 2. Ensembles of Classifiers . Traditional approach: Use one classifier. Determination . I. Fall . 2014. Professor Brandon A. Jones. Lecture 26: . Singular . Value . Decomposition and Filter Augmentations . Homework due Friday. Lecture quiz due Friday. Exam 2 – Friday, November 7. Weiqiang Dong. 1. Function Estimate . Input: . O. utput: . where . (“target function”) is a single valued deterministic function of . and . is a random variable,. The goal is to obtain an . estimate. Bias Variance Tradeoff. Guest Lecturer. Joseph E. Gonzalez. s. lides available here: . http://tinyurl.com/. reglecture. Simple Linear Regression. Y. X. Linear Model:. Response. Variable. Covariate. Slope. The Allan Variance Method. 0. You should be able to answer these questions…. PART I: MOTIVATION. What is noise?. What is noise modeling and why is it required?. PART . II: BASICS. How is noise characterized?. L. Boeckelmann (ECB). Rome – 14/11/2024. Discussion. 2. In a nutshell. This paper. Measure the . source-specific imported input content . of US establishments’ output and export using . confidential, micro-level datasets.