Bias variance trade off on maternal health risk dataset in K-NN and Decision tree algorithm
Harshil Panchal
, Hanoonah Sheikh , Shreyas Muchhal , Siddharth Kabra
K-NN, Decision Tree, Variance, Machine Learning
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.
"Bias variance trade off on maternal health risk dataset in K-NN and Decision tree algorithm", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 11, page no.18 - 22, November-2022, Available :https://ijsdr.org/papers/IJSDR2211004.pdf
Volume 7
Issue 11,
November-2022
Pages : 18 - 22
Paper Reg. ID: IJSDR_202424
Published Paper Id: IJSDR2211004
Downloads: 000347215
Research Area: Computer Science & Technology
Country: Indore, Madhya Pradesh, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
