Assessment of Supervised Machine Learning Algorithms

Neha Gahlan

Assessment of Supervised Machine Learning Algorithms

Authors: Neha Gahlan

Unique Paper ID: 149013
Volume: 6
Issue: 10
PageNo: 169-173

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.

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ISSN: 2349-6002
Volume: 6
Issue: 10
PageNo: 169-173

Assessment of Supervised Machine Learning Algorithms

Available:https://ijirt.org/Article?manuscript=149013

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8.01 (Year 2024)

UGC Approved
Journal no 47859

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