common class is selected as the prediction for all three observations. This causes a small increase in prediction variance in exchange for a small decrease in the prediction bias.

Model Shrinkage
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Since the choice of 50 features appears to be relatively arbitrary, we test DC-SIS on the full size 50 model and search for an optimal shrunken model. To do so, we can simply scan the performance of each size model between 1 and 50 and keep the smallest model that reaches a certain threshold. The threshold we have chosen is having estimated accuracy one standard error or less below the predicted accuracy of the size 50 model.

Results
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Prediction
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Using the same number of features as [@compare], DC-SIS was able to provide comparable predictive performance to the much more expensive mRMR selection as shown in Table \[tab:acc\]. The highest accuracy (0.86), F1 score (0.91), and Matthews correlation (0.60) were all achieved by the random forest.

Since generating the complete selection path with DC-SIS is as easy as computing a single selection, we also searched for the smallest model that had accuracy within one standard error ($\approx 0.01$) of the size 50 model. On average, this occurred 