Purpose

This analysis applies Naive Bayes classification to identify which student characteristics predict test preparation completion. The model was trained on 701 observations and tested on 299 to evaluate its ability to distinguish between students who completed versus did not complete test preparation, using three numeric predictors.

Key Findings

AUC (0.673): Acceptable discriminative ability—the model performs moderately better than random chance at ranking completed vs. non-completed students
Accuracy (0.629): The model correctly classifies approximately 63% of test cases, indicating moderate overall performance
Precision (0.484): When the model predicts completion, it is correct only 48% of the time, suggesting high false positive rates
Sensitivity (0.579): The model identifies 58% of students who actually completed preparation, missing 42% of true completers
Class Imbalance: 64.2% of students did not complete preparation versus 35.8% who did, reflecting an imbalanced dataset
Feature Separation: All three predictors show meaningful differences between completion groups (mean differences of 5–10 points), with predictor_3 showing the largest separation

Interpretation

The Naive Bayes model demonstrates moderate predictive capability but with notable limitations. The model is more conservative in predicting completion (