Improving junk mail identification and preventing it using ensemble decision tree in comparison with stochastic gradient descent Available to Purchase

Improving junk mail analysis from a combination of mail and blocking it is the determination of this research endeavor. Each of the two groups has a total of twenty samples. The second set of algorithms is called Stochastic Gradient Descent, and the first set is called Ensemble Decision Tree algorithms. There are ten samples in each of Groups 1 and 2, yielding an 80% G power and a 95% CI. The analysis and blocking of junk mail is accomplished using the Decision Tree algorithm and the Stochastic Gradient Descent technique, with unexpected model training and testing data values divided. Use of 64-bit system sort was made. The code implementation was carried out using Java. With α=0.05 and power=0.85, the computed Gpower test yielded an accuracy of approximately 85%. The decision tree method outperforms stochastic gradient descent with an accuracy of 87.8420% compared to 86.9790%, and the significance level is 0.000 (p<0.05). Here we see a graphical representation of a loss-means comparison between Decision Tree and Stochastic Gradient Descent methods. Compared to Stochastic Gradient Descent (13.1210%), Ensemble Decision Tree (12.158%) has a lower loss. When pitted against Stochastic Gradient Descent, the Decision Tree algorithm proves to be the more accurate of the two. Comparing the two methods, ensemble decision tree outperforms stochastic gradient descent. Additional topics covered include methods for enhancing precision and blocking the intrusion of spam into a system.