Assessment of Supervised Machine Learning Algorithms
Author(s):
Neha Gahlan
Keywords:
Decision Trees (DT), k-Nearest Neighbors (k-NN), Logistic Regression (LR), Random Forests (RF), Supervised Machine Learning, Support Vector Machine (SVM)
Abstract
Supervised machine learning is the assembly of algorithms that are able to produce general patterns and hypotheses by using superficially supplied instances to predict the fate of future instances. Supervised machine learning classification algorithms aim at categorizing data from prior information. Classification is carried out very frequently in data science problems. Various successful techniques have been proposed to solve such problems viz. Rule-based techniques, Logic-based techniques, Instance-based techniques, and stochastic techniques. This paper discusses the efficacy of supervised machine learning algorithms in terms of the accuracy, speed of learning, complexity and risk of over fitting measures. The main objective of this paper is to provide a general comparison with state of art machine learning algorithms.
