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README.md

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+---
+tags:
+- biology
+- genomics
+- classification
+- sklearn
+library_name: sklearn
+---
+
+# BioGuard DNA Classifier Ensemble
+
+This repository contains a dual-model ensemble for DNA sequence analysis and virus classification, trained using the **DNA Governance Console**.
+
+## 🧬 Models Included
+
+This repository hosts two distinct models specialized for different aspects of genomic analysis:
+
+### 1. **GenetiForest** (RandomForestClassifier)
+*   **File**: `dna_classifier.joblib`
+*   **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%
+
+### 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%
+*   **Classes**: Other, Influenza A, Chicken anemia virus, Norovirus, Influenza B
+
+## 🚀 Usage
+
+You can load these models using `joblib` in Python:
+
+```python
+import joblib
+
+# Load GenetiForest
+rf_model = joblib.load("dna_classifier.joblib")
+
+# Load ViralBoost
+gb_model = joblib.load("sequence_model.joblib")
+
+# Prediction
+# (Requires matching FeatureExtractor - see 'sequence_extractor.joblib')
+```
+
+## 📊 Training Meta
+*   **Generated By**: DNA Governance Console (vparka)
+*   **Framework**: scikit-learn