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Figure 1 | BMC Genomics

Figure 1

From: Scoring relevancy of features based on combinatorial analysis of Lasso with application to lymphoma diagnosis

Figure 1

Overview of bootstraping performed by FeaLect. A row and a column of the gray data matrix correspond to a feature and a case, accordingly. 1000 models are trained, each fitted to a random subset that contains 3 4 of cases using Lasso technique [1]. Without any assumption from a-priori knowledge, all features are included for training the models. Then the selected features are scored by computing an average vote (eq. 3) to select the most predictive ones.

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