Create GUIDE.md

GUIDE.md

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+You can explore datasets, visualize circuit QASM, and train a classical ML model to predict the noise type.
+
+---
+
+## 🔎 Explorer Tab
+
+The **Explorer** tab provides a preview of the dataset:
+
+1. **Dataset Dropdown** – Select one of the datasets:
+   - Core (Clean)  
+   - Depolarizing Noise  
+   - Amplitude Damping  
+   - Hardware-aware Noise  
+
+2. **Split Dropdown** – Select the data split (`train`, `test`, etc.).  
+3. **Preview Table** – Shows the first 10 circuits in the split.  
+4. **Raw QASM** – Original QASM for the selected circuit.  
+5. **Transpiled QASM** – QASM after transpilation, if available.  
+6. **Info Box** – Displays dataset name and other info.  
+7. **Summary Box** – Shows number of rows in the dataset.
+
+---
+
+## 🧠 Classification Tab
+
+The **Classification** tab allows you to train a Random Forest classifier on the selected features.
+
+1. **Input Features** – Select numeric features derived from the circuit:
+   - Adjacency features (density, degree mean, etc.)  
+   - QASM features (length, gate counts, measure count, etc.)  
+
+2. **Test Split** – Fraction of data used for testing (default `0.2`).  
+3. **Trees (n_estimators)** – Number of trees in the Random Forest.  
+4. **Max Depth** – Maximum tree depth. Critical parameter; increasing it may cause runtime issues.  
+5. **Random Seed** – Seed for reproducibility.  
+
+Click **Train & Evaluate** to:
+
+- Fit the classifier  
+- Compute metrics:
+  - Accuracy
+  - Macro F1
+  - Weighted F1  
+- Show confusion matrix  
+- Show top 10 feature importances  
+
+> ⚠️ Note: Max depth is the most influential hyperparameter. Setting it too high may crash the Space. Start with lower values.
+
+---
+
+## 📊 Output
+
+After training, you will see:
+
+1. **Confusion Matrix** – True vs predicted labels.  
+2. **Feature Importance** – Most relevant features for classification.  
+3. **Metrics** – Overall classification performance.
+
+---
+
+## 🔗 Links
+
+- [QSBench Website](https://qsbench.github.io)  
+- [Hugging Face Datasets](https://huggingface.co/QSBench)  
+- [GitHub Repository](https://github.com/QSBench)