at 23 features. The results of these reduced models are also included in Table \[tab:acc\]. As such, we can cut the model in half and still achieve results that are not statistically different from those achieved by mRMR as shown in Figure \[fig:acc.png\]. This is exemplified by the linear SVC that still achieved an accuracy of 0.84, F1 score of 0.90, and a Matthews Correlation of 0.54.

  --------------------------- ---------- --------------- --------------- ---------- --------------- --------------- ---------- --------------- ---------------
                              **mRMR**   **DC-SIS 50**   **DC-SIS 23**   **mRMR**   **DC-SIS 50**   **DC-SIS 23**   **mRMR**   **DC-SIS 50**   **DC-SIS 23**
  **Naive Bayes**             0.83       0.82            0.81            0.83       0.88            0.88            0.54       0.50            0.47
  **Logistic regression**     0.85       0.82            0.82            0.84       0.87            0.88            0.57       0.49            0.47
  **k-NN**                    0.85       0.84            0.82            0.82       0.90            0.88            0.56       0.51            0.46
  **Multilayer perceptron**   0.84       0.81            0.81            0.83       0.87            0.88            0.54       0.46            0.46
  **Random Forest**           0.85       0.84            0.82            0.84       0.90            0.89            0.57       0.54            0.50
  **SVC (Linear)**            0.83       0.83            0.84            0.82       0.90            0.90            0.52       0.52            0.54
  **SVC (RBF)**               0.86       0.84            0.82            0.84       0.90            0.89            0.59       0.53            0.48
  --------------------------- ---------- --------------- --------------- ---------- --------------- --------------- ---------- --------------- ---------------

Performance
-----------

By ignoring redundancy, DC-SIS does not get more expensive as the number of selected features grows. Since each predictor is 