Welcome to IJSDR UGC CARE norms ugc approved journal norms IJRTI Research Journal | ISSN : 2455-2631
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.15 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

Issue: November 2022

Volume 7 | Issue 11

Impact factor: 8.15

Click Here For more Info

Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter

Copyright Infringement Claims
Indexing Partner
Published Paper Details
Paper Title: Bias variance trade off on maternal health risk dataset in K-NN and Decision tree algorithm
Authors Name: Harshil Panchal , Hanoonah Sheikh , Shreyas Muchhal , Siddharth Kabra
Unique Id: IJSDR2211004
Published In: Volume 7 Issue 11, November-2022
Abstract: One of the most extensively used modelling strategies in the world of machine learning is classification algorithms like K- Nearest Neighbor (KNN) and DECISION TREE Algorithm. Machine learning models using classification algorithms are widely utilized in a variety of domains, including data analytics, image classification, computer vision, exploratory analysis, and game Artificial Intelligence, among others. In this case, the model must be extremely accurate, versatile, and efficient in order to successfully complete the work at hand. However, regardless of technique, the metrics used to assess a model's effectiveness are influenced by a variety of elements such as the confusion matrix, accuracy score, and so on. Among all these factors, the balance between bias and variance must be carefully maintained to optimize the model's performance. The KNN and DECISION TREE algorithms will be used to investigate bias and variance on the Maternal Health Risk Dataset in this research. Furthermore, the paper will focus on the regularization process and its impact on the balance of bias and variance, as well as how to deal with any inconsistencies that may develop owing to minor changes in dataset values. The benefits and drawbacks of variable bias and variance values, respectively, indicate the level of model adaptability on a dataset, regardless of how the training, testing, and validation data are divided.
Keywords: K-NN, Decision Tree, Variance, Machine Learning
Cite Article: "Bias variance trade off on maternal health risk dataset in K-NN and Decision tree algorithm", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 11, page no.18 - 22, November-2022, Available :http://www.ijsdr.org/papers/IJSDR2211004.pdf
Downloads: 000150694
Publication Details: Published Paper ID: IJSDR2211004
Registration ID:202424
Published In: Volume 7 Issue 11, November-2022
DOI (Digital Object Identifier):
Page No: 18 - 22
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

Click Here to Download This Article

Article Preview

Click here for Article Preview

Major Indexing from www.ijsdr.org
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

Track Paper
Important Links
Conference Proposal
DOI (A digital object identifier)

Providing A digital object identifier by DOI
How to GET DOI and Hard Copy Related
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Creative Commons License
This material is Open Knowledge
This material is Open Data
This material is Open Content
Social Media

Indexing Partner