PDF-2OnFeatureSelection,Bias-Variance,andBagging

Fig1BaggingperformancewithforwardstepwisefeatureselectionTheallfeatureslineshowsperformanceofbaggingwithall200featuresseparatingpointbetweenrelevantandirrelevantfeaturesWithtoomanyvariablesthei. 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. 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. Oliver Schulte. Machine Learning 726. Estimating Generalization Error. Presentation Title At Venue. The basic problem: Once I’ve built a classifier, how accurate will it be on future test data?. Problem of Induction: It’s hard to make predictions, especially about the future (Yogi Berra).. . . Dushaw Hockett. Executive Director. dushaw@thespacesproject.org. 202-360-7787. OBJECTIVES:. Introduce . the science of implicit bias;. Share . examples of how implicit bias shows up in daily life;. 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. What is bias anyway?. Favoring one side, position, or belief – being partial, prejudiced,. Bias. Bias …. is prejudice;  a preconceived judgment or an opinion formed without just grounds or sufficient knowledge . 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?.