Predictive model of failure devices using random forest comparing with logistic regression algorithm to improve the accuracy

The purpose of this work is to contrast the effectiveness of the Logistic Regression algorithm with that of Novel Random Forest when predicting device failures. Two groups, alpha (0.05), power (80%), and the environment ratio are utilised in the G power analysis to determine the necessary sample size. Twenty thousand datasets are taken from Kaggle as a representative sample. There is a 75% training dataset and a 25% test dataset created from the collected samples (25 percent). For the purpose of measuring Novel Random Forest’s efficacy, accuracy, precision, and sensitivity scores are computed. The model is significant at the (p 0.05) level. The Novel Random Forest model outperformed the Logistic Regression method by a wide margin, with an accuracy of 88.23%, precision of 87.43%, and sensitivity of 88.95%. The Logistic Regression method only managed 86.33% accuracy, 96.10% precision, and 83.30% sensitivity. It is determined that G power is 0. In this study, the Random Forest classifier outperforms the Logistic Regression classifier in terms of reducing costs for datasets that include failed devices.