pages/15_Metrics.py

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!")