--- a
+++ b/results/output_svm.txt
@@ -0,0 +1,38 @@
+[Creating the subsets for the values]
+[Creating the subsets for the labels "1"-"0"]
+
+[Optimization of the hyper-parameter k start]
+[Training the SVM model (with C=0.001) on training set & applying the SVM model to validation set]
+
+MCC = 0.5109884 (worst possible: -1; best possible: +1)
+When C=0.001, the MCC value is 0.5109884	 (worst possible: -1; best possible: +1)
+[Training the SVM model (with C=0.01) on training set & applying the SVM model to validation set]
+
+MCC = 0.5036179 (worst possible: -1; best possible: +1)
+When C=0.01, the MCC value is 0.5036179	 (worst possible: -1; best possible: +1)
+[Training the SVM model (with C=0.1) on training set & applying the SVM model to validation set]
+
+MCC = 0.5036179 (worst possible: -1; best possible: +1)
+When C=0.1, the MCC value is 0.5036179	 (worst possible: -1; best possible: +1)
+[Training the SVM model (with C=1) on training set & applying the SVM model to validation set]
+
+MCC = 0.5036179 (worst possible: -1; best possible: +1)
+When C=1, the MCC value is 0.5036179	 (worst possible: -1; best possible: +1)
+[Training the SVM model (with C=10) on training set & applying the SVM model to validation set]
+
+MCC = 0.5036179 (worst possible: -1; best possible: +1)
+When C=10, the MCC value is 0.5036179	 (worst possible: -1; best possible: +1)
+
+The best C value is 0.001, corresponding to MCC=0.510988410649606
+[Optimization end]
+
+[Training the SVM model (with the OPTIMIZED hyper-parameter C=0.001) on training set & applying the SVM to the test set]
+
+MCC = 0.5256819 (worst possible: -1; best possible: +1)
+
+f1_score = 0.5696361 (worst: 0.0; best: 1.0)
+accuracy = 0.8358066 (worst: 0.0; best: 1.0)
+
+true positive rate = recall = 0.4274953 (worst: 0.0; best: 1.0)
+true negative rate = specificity = 0.9749679 (worst: 0.0; best: 1.0)
+