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A Review Paper on Classification Models

Authors:

Ms. Shuchi Sharma, Abhishek, Akash kumar, Shahastransh Tiwari

Abstract — Classification models play a crucial role in various machine learning applications. This paper provides a comparative analysis of some of the widely-used classification algorithms (also referred to as models or machines). It includes definitions, examples, use cases and comparisons among the different models in order to differentiate among their pros and cons and determine the best one depending on the requirement.

The comparative analysis includes interpretability, accuracy and computational efficiency along with some other factors that can help differ one model from another.

Expectations generally foretell that a complex model will be more accurate but computation (time, memory and energy) intensive than a simpler model and thus, their use cases will be dependent on not just requirements but also resources and feasability. Results also indicate a similar result. This paper also involves a survey of Machine Learning students that tells us whether classification models are replaceable and how much are these models used in our daily needs and/or other sectors.

Keywords - Classification Models, KNN (K-Nearest Neighbours), SVM (Support Vector Machines), Random Forest, Accuracy, Precision, Recall, F1-Score

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