The most interesting thing to me was not only how wrong many of the data points were, but how many data points in the same category I matched. My income is one

The most interesting thing to me was not only how wrong many of the data points were, but how many data points in the same category I matched. My income is one of three different brackets, my age is one of three different brackets, my location is one of three different wrong places (which I would expect to be the easiest to get right?) It’s like there is some heuristic that cuts off the top 2-3 categories. Wish I had more data points to see if I am an outlier or if that is actually how the model constructs this bag of labels.