I am working on a prediction model to predict whether a stock should sell, hold or buy in
n days. Each day (or row in the dataset), I classify whether this should be sell, hold or buy based on the percentage change and a new column will be created to indicate what is the action for that particular day.
How should I deal with unbalance classification in my dataset when training my model? The train set as it is looks like this:
1 1401 0 835 -1 413 # 1 is buy, 0 is hold, -1 is sell
From reading up, balancing depends on the problem. Do I need to balance my data for a stock market prediction classification?
Thanks in advance.
PS: I am using SVM and Naive Bayes.
yis the binary classification of -1, 0 and 1. $\endgroup$