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README.md

@@ -7,12 +7,12 @@ tags:
 library_name: sklearn
 ---
 
-# BioGuard DNA Classifier Ensemble (Portable v1.1)
+# BioGuard DNA Classifier Ensemble (Portable v1.3)
 
 This repository contains a dual-model ensemble for DNA sequence analysis and virus classification, optimized for **portability and zero-dependency loading**.
 
 > [!NOTE]
-> **Version 1.1 Update**: This version has been refactored to decouple the models from custom feature extraction classes. It uses a raw scikit-learn format for maximum compatibility.
+> **Version 1.3 Update**: This version has been fully refactored to remove all custom class dependencies from `.joblib` files. Feature extraction is now strictly handled via source code.
 
 ## 🧬 Models Included
 
@@ -23,16 +23,16 @@ This repository hosts two distinct models specialized for different aspects of g
 *   **Purpose**: General-purpose synthetic vs. biological sequence classification.
 *   **Architecture**: Random Forest (sklearn) with biological feature extraction (k-mers, GC content, etc.).
 *   **Performance (Test Set)**:
-    *   **Accuracy**: 89.4%
-    *   **F1 Score**: 89.4%
+    *   **Accuracy**: 88.9%
+    *   **F1 Score**: 89.2%
 
 ### 2. **ViralBoost** (GradientBoostingClassifier)
 *   **File**: `sequence_model.joblib`
 *   **Purpose**: Specific virus type identification (Influenza A, Norovirus, etc.) based on sequence signatures.
 *   **Architecture**: Gradient Boosting (sklearn) trained on real-world viral sequences.
 *   **Performance (Test Set)**:
-    *   **Accuracy**: 99.4%
-    *   **F1 Score**: 99.4%
+    *   **Accuracy**: 99.3%
+    *   **F1 Score**: 99.3%
 *   **Classes**: Other, Influenza A, Chicken anemia virus, Norovirus, Influenza B
 
 ## 🚀 Usage