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Predicting Good Probabilities With Supervised Learning

www.cs.cornell.edu

predicting well-calibrated probabilities prior to calibration, but after calibration the best methods are boosted trees, ran-dom forests and SVMs. 2. Calibration Methods In this section we describe the two methods for mapping model predictions to posterior probabilities: Platt Calibra-tion and Isotonic Regression. Unfortunately, these methods

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SevenExcellence - Mettler Toledo

www.mt.com

agement and the sophisticated calibration support. Archiving results can be done automat-ically by creating a hardcopy on a METTLER TOLEDO or network printer and/or transfer to LabX® or EasyDirect™ pH PC software. The instrument provides calibra-tion support. Methods with a fixed calibration sequence, automatic evaluation of the calibration re-

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FERTILIZER SPREADER CALIBRA TION - Simplot

www.simplot.com

Method #1 1. Fill spreader with a weighed amount of fertilizer. 2. Layout calibration area in length. 100’, 50’, 200’, etc. 3. Apply the fertilizer the length of the run.

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Predicting Good Probabilities With Supervised Learning, Calibration, Methods, Calibration Methods, Calibra-tion, FERTILIZER SPREADER CALIBRA TION