feat: document models

research/models/svm_model.py

@@ -13,17 +13,23 @@ class SVMModel(TraditionalModel):
 
     def build_model(self) -> BaseEstimator:
         params = self.config.model_params
+        # TF-IDF downweights very common patterns; char n-grams (2,4) are effective
+        # for distinguishing name morphology under RBF kernels.
         vectorizer = TfidfVectorizer(
             analyzer="char",
             ngram_range=params.get("ngram_range", (2, 4)),
             max_features=params.get("max_features", 5000),
         )
 
+        # RBF kernel captures non-linear interactions between n-grams; probability=True
+        # adds calibration at some cost. Larger cache helps speed kernel computations.
         classifier = SVC(
             kernel=params.get("kernel", "rbf"),
             C=params.get("C", 1.0),
             gamma=params.get("gamma", "scale"),
             probability=True,  # Enable probability prediction
+            class_weight=params.get("class_weight", None),
+            cache_size=params.get("cache_size", 1000),
             random_state=self.config.random_seed,
             verbose=2,
         )