  Dataset pruning is the process of removing sub-optimal tuples from a dataset
to improve the learning of a machine learning model. In this paper, we compared
the performance of different algorithms, first on an unpruned dataset and then
on an iteratively pruned dataset. The goal was to understand whether an
algorithm (say A) on an unpruned dataset performs better than another algorithm
(say B), will algorithm B perform better on the pruned data or vice-versa. The
dataset chosen for our analysis is a subset of the largest movie ratings
database publicly available on the internet, IMDb [1]. The learning objective
of the model was to predict the categorical rating of a movie among 5 bins:
poor, average, good, very good, excellent. The results indicated that an
algorithm that performed better on an unpruned dataset also performed better on
a pruned dataset.
