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title: SMS Spam English Scikit-Learn
emoji: 🌖
colorFrom: gray
colorTo: green
sdk: streamlit
sdk_version: 1.17.0
app_file: app.py
pinned: false
license: openrail
ENGLISH The dataset used in the study "T.A. Almeida, J.M.G. Hidalgo, and A. Yamakami, Contributions to the Study of SMS Spam Filtering: New Collection and Results, Proc. 11th ACM Symposium on Document Engineering, pp. 259-262, 2011." is employed for training. The success ratio for Linear SVM Classifier is 0.9742 in terms of Macro-F1 when 10% of the dataset was used for testing. The dataset is composed of SPAM and LEGITIMATE sms data.
TÜRKÇE Bu çalışmada "T.A. Almeida, J.M.G. Hidalgo, and A. Yamakami, Contributions to the Study of SMS Spam Filtering: New Collection and Results, Proc. 11th ACM Symposium on Document Engineering, pp. 259-262, 2011." başlıklı çalışmadaki veri seti kullanılmıştır. Linear SVM sınıflandırıcı için başarı oranı, veri setinin %10'u test için kullanıldığında Makro-F1 açısından 0.9742'dir. Veri seti, SPAM ve LEGITIMATE kısa mesaj verilerinden oluşmaktadır.
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