feat: add Cohen's Kappa metric to classifier evaluation and output

main.py

@@ -95,6 +95,7 @@ def main():
     print(f"Training samples: {metrics['train_samples']}")
     print(f"Validation samples: {metrics['val_samples']}")
     print(f"Accuracy: {metrics['accuracy']:.2%}")
+    print(f"Cohen's Kappa: {metrics['kappa']:.4f}")
     print(f"Classes: {metrics['classes']}")
     print()
 

src/classifier.py

@@ -6,7 +6,7 @@ import numpy as np
 import rasterio
 from rasterio.transform import from_bounds
 from sklearn.model_selection import train_test_split
-from sklearn.metrics import classification_report, accuracy_score, confusion_matrix
+from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, cohen_kappa_score
 
 from .data import RasterData, VectorData, load_raster, load_vector, extract_raster_values_by_polygons
 from .strategies import ClassificationStrategy
@@ -80,11 +80,13 @@ class GISClassifier:
         # Evaluate
         y_pred = self.strategy.predict(X_val)
         accuracy = accuracy_score(y_val, y_pred)
-        
+        kappa = cohen_kappa_score(y_val, y_pred)
+
         return {
             "train_samples": len(X_train),
             "val_samples": len(X_val),
             "accuracy": accuracy,
+            "kappa": kappa,
             "classes": list(self._classes),
         }