Let me explain more about my question: I have collected 2000 data as the following:
age sex education residence music
young male Primary_school east mixture middle-agee female Primary_school north mixture young female Bachelor south_east mixture young male Master_degree east mixture old male physician east traditional middle-agee female Bachelor east mixture young female Bachelor center mixture teeneger female Primary_school west mixture young female Master_degree east mixture young female Master_degree south_east mixture young female Primary_school south mixture middle-agee female illiterate east mixture middle-agee male Bachelor south traditional young female Master_degree east mixture old male Primary_school south_east traditional middle-agee female Bachelor east traditional young female Master_degree south mixture ....
In our city, we have 22 districts with total population 12000,000 people. I have collected 2000 samples as the above from different locations by asking question from people each of the above question, like what is your age?, your education? your sex ? your district ( north, east, west,...) and what is your favorite music ( which kind of music do you like to listen)?
Now I want to use classification algorithm to predict if a new person selected from one area with specific age; sex; education; her residence (area), then we want to predict which kind of music she like to listen to?
I use R and examine 5 top algorithms such as svm, lda; knn; randon Forest and figured that the error is more than %70. [The OOB estimate of error rate is so high ~~%70]
Could you please explain why this bias and error happened?
Best regards Amir