Balanced Error Rate Ber
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pairs Connectomics Causality Challenge #1: Causation and balanced error rate wiki Prediction Synopsis Datasets Instructions Evaluation Submit Results Prizes Dissemination FAQ Analysis Evaluation
Bit Error Rate
The main objective of the challenge is to make good predictions of the target variable. The datasets were designed acceptable bit error rate such that the knowledge of causal relationships should help making better predictions in manipulated test sets. Hence, causal discovery is assessed indirectly via the test set performances or Tscore, which
Bit Error Rate Measurement
we will use for determining the winner. We also provide for information Fnum, Fscore, and Dscore, but will not use them for ranking participants. The scores found in the table of Results are defined as follows: Causal Discovery: Fnum: The number of features in [dataname]_feat.ulist or the best number of features used to make predictions with [dataname]_feat.slist. Fscore: Score for the bit error rate pdf list of features provided (see details below). For sorted lists [dataname]_feat.slist, the most predictive features should come first to get a high score. For unsorted lists [dataname]_feat.ulist, the features provided should be highly predictive to get a high score. Target Prediction: Dscore: Discovery score evaluating the target prediction values [dataname]_train.predict. Tscore: Test score evaluating the target prediction values [dataname]_test.predict. Presently, for the datasets proposed, the Tscore and Dscore are the training and test AUC (which are identical to the BAC in the case of binary predictions). During the development period, the scores are replaced by xxxx, except for the toy datasets, which do not count for the competition. A color coding indicates in which quartile your scores lie. The actual results will be made visible only after the end of the challenge. Performance Measure Definitions The results of classification, obtained by thresholding the prediction values made by a discriminant classifier, may be represented in a confusion matrix, where tp (true positive), fn (false negative), tn (true negative) and fp (false positive) represent the number of examples falling into each possible outcome
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Earn by Contributing Earn Free AccessLearn More > Upload Documents Write Course Advice Refer your Friends Earn MoneyLearn More > Upload bit error rate tester software Documents Apply for Scholarship Create Q&A pairs Become a Tutor Are you an educator? Log in Sign up Home Hanoi University of Technology CS CS 329 Group Sparse Additive Models.pdf Ber refers to the http://www.causality.inf.ethz.ch/challenge.php?page=evaluation balanced error rate which is SCHOOL Hanoi University of Technology COURSE TITLE CS 329 TYPE Lab Report UPLOADED BY tungngthanh PAGES 8 Click to edit the document details This preview shows pages 7–8. Sign up to view the full content. View Full Document cancer dataset. BER refers to the balanced error rate, which is defined as the average of the errors in each tumor type. #genes denotes the number of https://www.coursehero.com/file/pqk3cp/BER-refers-to-the-balanced-error-rate-which-is-de%EF%AC%81ned-as-the-average-of-the/ distinct selected genes. #pathways denotes the number of selected pathways. samples. To get the classification label from the non- parametric regression analysis, we simply take the sign of the predicted responses. Table 3 shows the results of GroupSpAM, SpAM and group lasso with over- lap ( Jacob et al. , 2009 ) based on the balanced loss function by a 3-fold cross validation 2 . As we can see 2 When running COSSO, we ran into the same problem as in the simulation. Hence we left the results of COSSO. This preview has intentionally blurred sections. Sign up to view the full version. View Full Document Group Sparse Additive Models from Table 3 , compared to SpAM, it achieves simi- lar balanced error rates but with less selected genes and pathways, which could lead to an easier interpre- tation for genetic functional analysis. As compared to group lasso, GroupSpAM has an improved balanced error rate ( P = 0 . 054), suggesting that a better pre- dictive model can be built by using the more flexi- ble additive model class. The functional relationship of the identified genes and pathways to breast cancer merits further investigation. 7. Conclusions In this paper, we propose a novel method for variable selec
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