feat: Phase 3A statistical device-category classifier (86.8% top-1 on RAW)

Trains sklearn RandomForest + GradientBoosting on 14 timing/statistical
features from RAW-only UberGuidoZ captures, with a group-aware split
(GroupShuffleSplit by device sub-folder) so near-duplicate captures never
leak across train/test. The heuristic CategoryRouter is scored on the exact
same held-out files for a fair comparison.

Best model (gradient_boost): 86.8% top-1 accuracy, 62.5% balanced accuracy
vs heuristic 30.0% top-1 / 55.9% routed. Raw accuracy is inflated by the
70%-Garage RAW class balance; balanced accuracy (~2x the heuristic) is the
honest number and meets the Phase 3A 60-75% target. Not yet wired into the
decoder ensemble.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

models/category_classifier_metrics.json

@@ -0,0 +1,47 @@
+{
+  "best_model": "gradient_boost",
+  "n_samples": 803,
+  "n_train": 583,
+  "n_test": 220,
+  "features": [
+    "freq_mhz",
+    "short_pulse_us",
+    "long_pulse_us",
+    "pulse_ratio",
+    "short_gap_us",
+    "long_gap_us",
+    "gap_ratio",
+    "duty_cycle",
+    "pulse_count",
+    "pulse_mean_abs",
+    "pulse_std_abs",
+    "pulse_min_abs",
+    "pulse_max_abs",
+    "preamble_type"
+  ],
+  "class_balance": {
+    "Doorbell": 39,
+    "Garage Door Opener": 565,
+    "Weather Sensor": 12,
+    "Fan Controller": 91,
+    "Security Sensor": 2,
+    "Remote Control": 94
+  },
+  "metrics": {
+    "random_forest": {
+      "accuracy": 0.8272727272727273,
+      "balanced_accuracy": 0.5789748226457088,
+      "macro_f1": 0.5360433604336042
+    },
+    "gradient_boost": {
+      "accuracy": 0.8681818181818182,
+      "balanced_accuracy": 0.6253728690437551,
+      "macro_f1": 0.5340975664713835
+    }
+  },
+  "heuristic_baseline_same_test": {
+    "top1": 0.3,
+    "routed": 0.5590909090909091,
+    "n_test": 220
+  }
+}