PDF-Bias-Variance Tradeoff and Ensemble Methodsreadings: handed out in cla

1 Rich Maclin BiasVariance Decomposition for RegressionBiasVariance Analysis of Learning AlgorithmsEnsemble MethodsEffect of Bagging on Bias and Variance Example 20 pointsy x 2 sin15x N0. 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. Ensembles. CS 478 - Ensembles. 2. A “Holy Grail” of Machine Learning. Automated. Learner. Just a . Data Set. or. just an. explanation. of the problem. Hypothesis. Input Features. Outputs. CS 478 - Ensembles. Applying data assimilation for rapid forecast updates in global weather models. Luke E. Madaus --- Greg Hakim; Cliff Mass. University of Washington. In Revision -- QJRMS. Outline. Brief introduction. 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. Research Supported by: . Indian Council of Medical Research’s Short Term Studentship-2014. Author: Apurva Lunia. (2. nd. . Prof. M.B.B.S. .).  . Guide: Dr . . D.S. . . Choudhary. ,. M.B.B.S., M.S. . 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).. james.d.brown@noaa.gov. Verification of ensemble streamflow forecasts using the Ensemble Verification System (EVS). AMS pre-conference workshop . 23. rd. Jan. 2010. 2. Overview. 1. Brief review of the NWS HEFS. 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. Chong Ho (Alex) Yu. Problems of bias and variance. The bias is . the . error which results from missing a target. . For . example, if an estimated mean is 3, but the actual population value is 3.5, then the bias value is 0.5. . Zhiqi. Peng. Key concepts of supervised learning. Objective function:. is training loss, measure how well model fit on training data. is regularization, measures complexity of model.  . Key concepts of supervised learning. 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. . Pillai. 2. , Andrew Turner. 1. , . Gill . Martin. 3. , . Steve . Woolnough. 1, . E. N. Rajagopal. 2. 1. NCAS-Climate, University of Reading. 2. NCMWRF. 3. Met Office. Evaluation and improvement of . Categorical Data Methods. This Lecture. Judy Zhong Ph.D.. Outline. Categorical data. Definition. Contingency table. Example. Pearson’s . . 2. test for goodness of fit. . 2 . test for two population proportions.