docs: use generic predictor terminology in training output

router.py

@@ -137,7 +137,7 @@ class R2Router:
         with open(os.path.join(path, "training_data", "labels.json")) as f:
             labels = json.load(f)
 
-        print(f"Training KNN (k={k}) on {len(X_train)} samples...")
+        print(f"Training router on {len(X_train)} samples (k={k})...")
 
         quality_knns = {}
         token_knns = {}
@@ -173,7 +173,7 @@ class R2Router:
                     token_knns[model_name] = tknn
                     n_token += 1
 
-        print(f"Trained {n_quality} quality KNNs + {n_token} token KNNs for {len(quality_knns)} models.")
+        print(f"Trained {n_quality} quality predictors + {n_token} token predictors for {len(quality_knns)} models.")
 
         model_prices = {
             mn: cfg["output_price_per_million"]