I'm doing data analysis with a dataset of 11795 data points (with 88 features). 85% (9973 points) of these data points correspond to data points belonging to class 1, 5% (589 points) belong to class 2 and 10% (1233 points) belong to class 3.
I'm trying to build a model from this data for predicting the class of new data points. I started to wonder if I build my model using this dataset, does it favour the class 1 data points? Would it be difficult for the model to detect the low frequency classes?
Generally how does one tackle unbalanced data sets such as the one I have?
Thank you for any advices =)
I'm using k-nearest neighbor and regularized linear regression methods.