I am working on a classification model using one of the following three algorithms: RandomForestClassifier, a TensorFlow model and a LogisticRegression model.
The data set I am working with has a feature that is represented by a single word that uses ASCII characters (may or may not be a valid word in any language). I don't see any advantage in treating this column as categorical data since
number of unique words/total number of rows is very close to 1, i.e., almost every word is unique.
Is there any obvious way to use this column to improve the predictive capabilities of the resulting classification model?
The data I am working with are player IDs that are strings and are meaningless in any language. But would the answer to the above question change if I were working with single English words?