  This paper presents the contribution of our dzNLP team to the NADI 2024
shared task, specifically in Subtask 1 - Multi-label Country-level Dialect
Identification (MLDID) (Closed Track). We explored various configurations to
address the challenge: in Experiment 1, we utilized a union of n-gram analyzers
(word, character, character with word boundaries) with different n-gram values;
in Experiment 2, we combined a weighted union of Term Frequency-Inverse
Document Frequency (TF-IDF) features with various weights; and in Experiment 3,
we implemented a weighted major voting scheme using three classifiers: Linear
Support Vector Classifier (LSVC), Random Forest (RF), and K-Nearest Neighbors
(KNN).
  Our approach, despite its simplicity and reliance on traditional machine
learning techniques, demonstrated competitive performance in terms of F1-score
and precision. Notably, we achieved the highest precision score of 63.22% among
the participating teams. However, our overall F1 score was approximately 21%,
significantly impacted by a low recall rate of 12.87%. This indicates that
while our models were highly precise, they struggled to recall a broad range of
dialect labels, highlighting a critical area for improvement in handling
diverse dialectal variations.
