library(tidyverse) library(lubridate) library(ggplot2) library(naivebayes) w.start() SSEC<-w.wsd("000001.SH","open,high,low,close","2010-01-01","2020-07-18") SSEC<-xts(SSEC$Data[,2:5],order.by=as.Date(SSEC$Data[
As the Naive Bayes algorithm has the assumption of the “Naive” features it performs much better than other algorithms like Logistic Regression, Tree based algorithms etc. The Naive Bayes classifieris much faster with its probability calculations. Why do naive Bayesian classifiers perform so well?
The Naive Bayes algorithm did not require hyperparameter tuning, and its default settings were used. It still achieved a respectable \({F}_{1}\) score of 0.94. Finally, the Logistic Regression algorithm was tuned for the hyperparameters regularization strength, max_iter, and penalty. The best...
the? size? of? the? training? data? to? the? accuracy? of? the classification.? The? results? suggest? that? C4.5? algorithm produces?? highest classification?? accuracy?? at?? the?? order of? 81%? followed? by? the? methods? of? Naive? Bayes? 76% and? Nearest? Neighbor? 55%...
Nevertheless, the Naive Bayes algorithm has been shown time and time again to perform really well in classification problems, despite the assumption of independence. Simultaneously, it is a fast algorithm since it scales easily to include many predictors without having to handle multi-dimensional...
Naïve Bayes algorithm was adopted for a specific amount of water according to the crop needs. By adopting this proposed method, proper water management can be achieved. In a different study, researchers (Xie et al., 2017) suggested a framework to support irrigation systems. The framework ...
Cybersecurity is crucial in today’s interconnected world, as digital technologies are increasingly used in various sectors. The risk of cyberattacks
[57]. Cost-sensitive methods utilize the degree of imbalance to create cost matrices that account for misclassification penalties. The goal of algorithm-level methods is to adapt classification algorithms to better handle imbalance. The data-level method is the most prevalent approach, as it ...
Naive Bayes (NB) is a well known statistical learning algorithm recommended as a base level classifier for comparison with other algorithms (Guyon, 2009, Henery, 1994). NB estimates class-conditional probabilities by “naively” assuming that for a given class the inputs are independent of each...
Lack of Explainability: Many generative AI models function as ‘black boxes,’ making it difficult to understand or explain their decision-making processes. Even the engineers or data scientists who develop the underlying algorithm may not fully comprehend or articulate the exact processes inside it ...