So, I'm a newbie in ML field and I try to do some classification. My goal is to predict the outcome of a sport event. I've gathered some historical data and now try to train a classifier. I got around 1200 samples, 0.2 of them I split off for test purposes, others I put into grid search (cross-validation included) with different classifiers. I've tried SVM with linear, rbf and polynominal kernels and Random Forests to the moment. Unfortunately, I can not get accuracy significantly larger than 0.5 (the same as random choice of class). Does it mean I just can't predict outcome of such a complex event? Or I can get at least 0.7-0.8 accuracy? If it's feasible, then what should I look into next?
- Get more data? (I can enlarge dataset up to 5 times)
- Try different classifiers? (Logistic regression, kNN, etc)
- Reevaluate my feature set? Are there any ML-tools to analyze, which features make sense and which don't? Maybe, I should reduce my feature set (currently I have 12 features)?