This study utilizes software sizing metrics, effort metrics, and defect density information, and focuses on developing defect prediction models that apply various machine learning...

In this work we have investigated two data mining techniques: the Naïve Bayes and the C4.5 decision tree algorithms. The goal of this work is to predict whether a client will subscribe a term deposit. We also made comparative study of performance of those two algorithms.

Paper also presents a software bug prediction model based on supervised machine learning algorithms are Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF) and Logistic Regression (LR) on four datasets.

This paper describes a study that built a neural network prediction model based on feature extraction, focusing on text analysis and image analysis of WeChat official accounts reading quantity.

Naive Bayes classifier assumes that the effect of the value of a predictor (x) on a given class (c) is independent of the values of other predictors. This assumption is called class...

Apr 2, 2025 · Naive Bayes classifiers are supervised machine learning algorithms used for classification tasks, based on Bayes’ Theorem to find probabilities. This article will give you an overview as well as more advanced use and implementation of Naive Bayes in machine learning.

Nov 4, 2018 · Naive Bayes is a probabilistic machine learning algorithm that can be used in a wide variety of classification tasks. Typical applications include filtering spam, classifying documents, sentiment prediction etc.

In this study, we assess the performance of 11 ML algorithms on four diabetes prediction datasets, considering the top 2, top 3, and all attributes.

My study seeks to cover multiple facets of software bug prediction, with the goal to synthesize, scrutinize, and appraise the machine learning methods employed thus far in the discipline. I will examine the datasets applied to the models, the software metrics typically employed, and the measures used to gauge model performance.

In this paper, the author presents a model that will predict the bugs with the help of machine learning classifiers. For this model, the researcher has used the dataset NASA from the known repositories and used two supervised ML classifier algorithms such as linear supervision (LR) and Naive Bayes (NB) for detecting and predicting faults.