The classification problem in hand is to detect an event that should occur (target=1) within the next forward window of fw days using the current environment state which contains 26 features.
The dataset is unbalanced, with 508 event occurrences (target=1) vs. 3024 non-occurrences (target=0).
The dataset was normalized (min-max-scaling) and the SVM was trained on 70% of the dataset. Classifying the unseen data returned class 0 for all, however about 12% should be 1.
I first suspected that this is due to the dataset being biased towards non-occurrences, so I balanced the dataset by up-sampling occurrences, however the same result was obtained.
A grid search on C and gamma for the RBF kernel was performed, each time the classification is 0.
Is it possible that the result obtained is because the input features are not sufficient to detect the event occurrence? Or is there something else the could be experimented with prior to changing the dataset features?