Update pages/15_Metrics.py

pages/15_Metrics.py

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+import streamlit as st
+
+st.set_page_config(page_title="Model Evaluation Metrics", page_icon="📊", layout="wide")
+
+st.sidebar.title("📊 Model Evaluation Metrics")
+st.sidebar.markdown("Select a metric category from below.")
+
+st.markdown("<h1 style='text-align: center;'>📏 Model Evaluation Metrics</h1>", unsafe_allow_html=True)
+
+metric_type = st.radio(
+    "Select the type of model evaluation:",
+    ["🎯 Classification Metrics", "📈 Regression Metrics"]
+)
+
+if metric_type == "🎯 Classification Metrics":
+    st.markdown("## 🎯 Classification Metrics")
+    st.write("Used when the target variable is **categorical**.")
+
+    st.markdown("### 1. Accuracy")
+    st.write("""
+    - **Definition**: Correct predictions out of total predictions  
+    - **Formula**:  
+      Accuracy = (TP + TN) / (TP + FP + FN + TN)  
+    - ⚠️ Avoid using when classes are imbalanced.
+    """)
+
+    st.markdown("### 2. Confusion Matrix")
+    st.write("""
+    A matrix that compares actual and predicted labels.  
+    Useful for understanding **true positives**, **false positives**, **true negatives**, and **false negatives**.
+    
+    |               | Predicted Positive | Predicted Negative |
+    |---------------|--------------------|--------------------|
+    | Actual Positive | True Positive (TP) | False Negative (FN) |
+    | Actual Negative | False Positive (FP) | True Negative (TN) |
+    
+    - Use for binary and multiclass classification.
+    """)
+
+    st.markdown("### 3. Precision")
+    st.latex(r"Precision = \frac{TP}{TP + FP}")
+    st.write("Of all predicted positives, how many were correct.")
+
+    st.markdown("### 4. Recall (Sensitivity)")
+    st.latex(r"Recall = \frac{TP}{TP + FN}")
+    st.write("Of all actual positives, how many were correctly identified.")
+
+    st.markdown("### 5. F1 Score")
+    st.latex(r"F1 = 2 \cdot \frac{Precision \cdot Recall}{Precision + Recall}")
+    st.write("Harmonic mean of precision and recall. Good for imbalanced classes.")
+
+    st.markdown("### 6. Specificity (True Negative Rate)")
+    st.latex(r"Specificity = \frac{TN}{TN + FP}")
+    st.write("Measures how well the model identifies negatives.")
+
+    st.markdown("### 7. ROC Curve and AUC")
+    st.write("""
+    - **ROC Curve**: Plot of True Positive Rate (Recall) vs False Positive Rate  
+    - **AUC** (Area Under the Curve): Measures model's ability to distinguish classes.  
+      - AUC = 1: Perfect  
+      - AUC = 0.5: Random
+    """)
+
+    st.markdown("### 8. Log Loss (Logarithmic Loss)")
+    st.latex(r"LogLoss = -\frac{1}{n} \sum \left[ y \log(\hat{y}) + (1 - y) \log(1 - \hat{y}) \right]")
+    st.write("""
+    - Evaluates predicted probabilities instead of just labels  
+    - Lower log loss indicates better performance  
+    - Especially useful for probabilistic models
+    """)
+
+elif metric_type == "📈 Regression Metrics":
+    st.markdown("## 📈 Regression Metrics")
+    st.write("Used when the target variable is **continuous**.")
+
+    st.markdown("### 1. Mean Absolute Error (MAE)")
+    st.latex(r"MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|")
+    st.write("Measures average absolute difference between actual and predicted values. More robust to outliers.")
+
+    st.markdown("### 2. Mean Squared Error (MSE)")
+    st.latex(r"MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2")
+    st.write("Penalizes large errors more than MAE. Sensitive to outliers.")
+
+    st.markdown("### 3. Root Mean Squared Error (RMSE)")
+    st.latex(r"RMSE = \sqrt{MSE}")
+    st.write("Square root of MSE. Easy to interpret since it has same units as output.")
+
+    st.markdown("### 4. R² Score (Coefficient of Determination)")
+    st.latex(r"R^2 = 1 - \frac{SS_{res}}{SS_{tot}}")
+    st.write("""
+    Indicates how well model explains variation in data:  
+    - **1.0** → perfect  
+    - **0.0** → same as predicting mean  
+    - **< 0** → worse than mean
+    """)
+
+    st.markdown("### 5. Adjusted R² Score")
+    st.latex(r"\text{Adjusted } R^2 = 1 - \left( \frac{(1 - R^2)(n - 1)}{n - k - 1} \right)")
+    st.write("""
+    - Adjusts R² for number of predictors (k)  
+    - Prevents overestimating performance from adding irrelevant features
+    """)
+
+    st.markdown("### 6. Mean Absolute Percentage Error (MAPE)")
+    st.latex(r"MAPE = \frac{100\%}{n} \sum_{i=1}^{n} \left| \frac{y_i - \hat{y}_i}{y_i} \right|")
+    st.write("Expresses error as a percentage. Avoid if actual values can be 0.")
+
+    st.markdown("### 7. Median Absolute Error")
+    st.write("Robust metric not influenced by outliers. Takes the median of all absolute differences.")
+
+st.markdown("---")
+st.markdown("### ✅ Choosing the Right Metric")
+st.write("""
+- **Classification**:
+    - Use **F1-score** for imbalanced data.
+    - Use **AUC-ROC** for probabilistic classifiers.
+    - Use **Log Loss** if working with predicted probabilities.
+- **Regression**:
+    - Use **RMSE** when large errors are more serious.
+    - Use **MAE** when all errors matter equally.
+    - Use **R²** to evaluate explained variance.
+- Always compare with a **baseline model**.
+""")
+
+st.success("Choosing the right metric helps you evaluate and improve your model with confidence!")