This is my first foray into machine learning, as I usually rely on statistical methods.
In the past I've used SVM and its worked, but this is usually with data that is classification (e.g. iris and spam data). Currently, I am trying to using SVM to predict hourly wind speed data. Recorded wind speed can run anywhere from 0 to 45 in this data set, so essentially 45 categories.
Below is my code, which all works completely fine. The data set I'm using is an xts object which runs from 2016/01/01 to 2017/04/31 (20,423 observations). Aside from wind speed, the data set also contains 15 predictor variables which I include in the model.
svmtrain <- window(wdsp.ts.sub, end = as.POSIXct("2016-03-31 23:00:00")) svmtest <- window(wdsp.ts.sub, start = as.POSIXct("2016-04-01 00:00:00")) # run kernel SVM fitsvm <- ksvm(wdsp ~ ., data = svmtrain) predsvm <- predict(fitsvm, svmtest[,2:16]) svmtab <- table(svmtest$wdsp, predsvm)
The last line of code is how I would normally compute my accuracy table, but it doesn't really make sense in this case.
How can I compute how accurate this model is, especially in comparison with something more intuitive/easy to understand like an ARIMA?