A Comparative Analysis of Classification Algorithms for Extrovert and Introvert Identification
Annotatsiya
The work outlines a model using supervised machine learning for the classification of personalities prediction, or the ability to distinguish between both types of personalities, based on behavioral data containing seven characteristics, such as social event attendance, stage fright, and time spent alone. The 2,900 samples of data will be one-hot encoded for categorical variables and standardized for numerical variables. Logistic Regressive, The use of support vector machines (SVM), Random Forest (RF), XGBoost, a Naive Bayes model, and Choice Tree were from the classification models subsequently employed, and GridSearchCV was used to fine-tune the pipelines. Gradient-based models with learning rates between 0.01 and 0.2 and a batch size of 32 were used for training. The best-performing models were Logistic Regression and the tuned models were convergent to 92.93, while the default scores varied between 87.24 to 92.93. Metrics for assessment such as the F1-score (0.93) and ROC-AUC (0.93) attest to the reliability and durability of the suggested personality rating method.