This paper presents methods for filtering spam messages using machine learning models in artificial intelligence. Machine learning algorithms are widely used for automatic spam detection because they can learn from large volumes of data and effectively classify new messages. Therefore, the most commonly used machine learning algorithms for spam filtering, namely Naive Bayes, Decision Tree, Random Forest, and Support Vector Machine (SVM), are examined. Additionally, the differences between the models, their advantages, and limitations are identified
| Mualliflar | Atajanov, Muzaffar, Ibroximali, Normatov |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2025-12-10 |
| Son | 4 |
| Betlar | 78-82 |
| Til | Rus |
TF-IDF, Root node, Leaf nodes, Precision, Recall, F1-score, spam, ham., TF-IDF, Root node, Leaf nodes, Precision, Recall, F1-score, spam, ham
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