I have an deeply understanding-problem with Random Forest Regression. Target is a university project: We have to do a random forest regression analytics with financial data in R. I already read many hours in random forest examples, most of them are classification type like to predict a stock value is go up or down. In case of regression I stand on the line .. My thoughts are the following:
If I had a data set like the following structure:
Date | Open | High | Low | Close | Volume
...I could add some technical instruments as RSI, SMA and so on
Then I split the data set to training and test data, execute the random forest procedure and close this with predicting on the test data. But is this really the intent of a regression analytics with random forest? I guess a 'correct' regression analysis is to compare two stocks and see if they correlate to predict the value of one stock based on the other stock - but on the other side this is a classical regression analysis - in the absence of Random Forest Algorithm. I have a really big problem with understanding the purpose ...