I have a data set of movies and their subtitles.My task is to classify them based on their ratings-[R,NR,PG,PG-13,G]. I have tried different ML algorithms and found that Logistic regression out performed them all, but I am unable to figure out why.My data had more features than observations.
SVM- should perform well on high dimensional data and will perform well even the there is class imbalance, but failed to show great results. Naive Bayes-I think Naive Bayes did not perform well because of class imbalance. Random forest-decent performance.but did not out perform logistic regression.
I am looking for an explanation as to why did one perform better than the other.
Note:The data set is sparse and it has more features/parameters than observations/examples.