Are You an Introvert or Extrovert? Accurate Classification With Only Ten Predictors
This addresses the need for efficient personality assessment tools, though it is incremental as it builds on existing data and methods.
The paper tackled the problem of predicting introversion vs. extroversion with minimal predictors, achieving 73.81% accuracy on unseen data by reducing features from 94 to 10 with only a 1% performance loss.
This paper investigates how accurately the prediction of being an introvert vs. extrovert can be made with less than ten predictors. The study is based on a previous data collection of 7161 respondents of a survey on 91 personality and 3 demographic items. The results show that it is possible to effectively reduce the size of this measurement instrument from 94 to 10 features with a performance loss of only 1%, achieving an accuracy of 73.81% on unseen data. Class imbalance correction methods like SMOTE or ADASYN showed considerable improvement on the validation set but only minor performance improvement on the testing set.