  In this research, we aim to compare the performance of different classical
machine learning models and neural networks in identifying the frequency of
occurrence of each digit in a given number. It has various applications in
machine learning and computer vision, e.g. for obtaining the frequency of a
target object in a visual scene. We considered this problem as a hybrid of
classification and regression tasks. We carefully create our own datasets to
observe systematic differences between different methods. We evaluate each of
the methods using different metrics across multiple datasets.The metrics of
performance used were the root mean squared error and mean absolute error for
regression evaluation, and accuracy for classification performance evaluation.
We observe that decision trees and random forests overfit to the dataset, due
to their inherent bias, and are not able to generalize well. We also observe
that the neural networks significantly outperform the classical machine
learning models in terms of both the regression and classification metrics for
both the 6-digit and 10-digit number datasets. Dataset and code are available
on github.
