Accuracy improvement for personality prediction using logistic regression in comparison with random forest algorithm.

The research aims to discern people personality types by examining four dimensions of personality traits derived from their cognition and ideas through the application of logistic regression and Random Forest methodologies. Two cohorts were established, with one cohort implementing Logistic Regression and the other cohort adopting Random Forest. The cohorts underwent around 38 iterations. The sample size was established using a personality prediction analysis with a significance level (alpha) of 0.005, a pretest power of 80%, and a confidence level of 95%. A Myers-Briggs Type Indicator (MBTI) dataset, containing 8675 samples, was divided into two sets: 6000 samples for training and 2700 samples for testing. The simulation using Logistic Regression produced a personality prediction accuracy of 97.86%, whereas Random Forest achieved an accuracy of 82.18%. The independent sample T-test resulted in a p-value of 0.003, which indicates statistical significance at a significance level of p<0.05. These findings indicate that Logistic Regression performs much better than Random Forest in predicting personality based on the supplied dataset. Logistic Regression achieves an improved accuracy rate of 97.86% compared to Random Forest. [ABSTRACT FROM AUTHOR]