Analysis of K neighbors classifier algorithm compared for improved accuracy with logistic regression for predicting depression

Biyyapu Mahesh; M. Amanullah

doi:10.1063/5.0265342

ScienceGate Book Chapters

JOURNAL ARTICLE

Analysis of K neighbors classifier algorithm compared for improved accuracy with logistic regression for predicting depression

Biyyapu Mahesh M. Amanullah

Year: 2025 Journal: AIP conference proceedings Vol: 3267 Pages: 020114-020114 Publisher: American Institute of Physics

DOI: 10.1063/5.0265342

Get Full-Text PDF Get Analytical Report

Keywords:

Logistic regression Computer science Logistic model tree Classifier (UML) Artificial intelligence Regression Pattern recognition (psychology) Statistical classification Algorithm Statistics Machine learning Mathematics

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.18

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Analysis of K neighbors classifier algorithm compared for improved accuracy with logistic regression for predicting depression

Metrics

Topics

Related Documents

Improved Accuracy for prediction of leaf wetness using Logistic Regression algorithm compared with Decision Tree algorithm

Comparative analysis of Gaussian Naive Bayes algorithm with k-neighbors classifier with improved accuracy for predicting text based hate speech recognition

Analysis of Automobile Wheel Counting using Novel adaboosting Algorithm with Accuracy Compared to Logistic Regression Algorithm

Prediction of Depression using the KNeighbors-Classifier Algorithm Compared for Improved Accuracy with Support Vector Machine

Efficient Human Action Recognition using Novel Logistic Regression Compared over Linear Regression with Improved Accuracy