朴素贝叶斯分类器
逻辑回归
计算机科学
人工智能
贝叶斯定理
机器学习
Bayes错误率
样品(材料)
样本量测定
统计
数据挖掘
贝叶斯分类器
数学
支持向量机
贝叶斯概率
化学
色谱法
作者
V. Siva Malleswar Reddy,T. Poovizhi
标识
DOI:10.1109/icbats54253.2022.9759006
摘要
To Enhance the accuracy performance in mining twitter data movie reviews. Naive Bayes with sample size of (N=5) and Logistic Regression with sample size of (N=5) was iterated at different times for prediction accuracy performance of movie reviews. The sigmoid function used in Naive Bayes Prediction to probability which helps to improve the prediction of accuracy. There was a statistical significance between Naive Bayes and Logistic Regression (p=0.00). Result proved that the Naive Bayes got significant result with 91% accuracy compared to Logistic Regression with 63% accuracy. Naive Bayes is a simple and most effective algorithm to build fast machine learning models. Naive Bayes helps predicting with more accuracy percentage of movie review.
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