diff --git a/notebooks/XGBWW_Catalog_Random100_XGBoost_Accuracy_WithOverfitCatalog.ipynb b/notebooks/XGBWW_Catalog_Random100_XGBoost_Accuracy_WithOverfitCatalog.ipynb
index 07bfd47..f217081 100644
--- a/notebooks/XGBWW_Catalog_Random100_XGBoost_Accuracy_WithOverfitCatalog.ipynb
+++ b/notebooks/XGBWW_Catalog_Random100_XGBoost_Accuracy_WithOverfitCatalog.ipynb
@@ -5560,6 +5560,113 @@
     "        plt.show()\n",
     ""
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 12) Well-fit scatter grid: WW metrics vs generalization gap (per dataset \u00d7 3 metrics)\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "if 'combined_df' not in globals() or combined_df.empty:\n",
+    "    print('No combined results available. Run the training/aggregation cells first.')\n",
+    "else:\n",
+    "    scatter_df = combined_df.copy()\n",
+    "    scatter_df['case_type'] = scatter_df.get('case_type', 'good').fillna('good').astype(str)\n",
+    "    scatter_df['overfit_mode'] = scatter_df.get('overfit_mode', 'none').fillna('none').astype(str)\n",
+    "\n",
+    "    good_df = scatter_df[scatter_df['case_type'] != 'overfit'].copy()\n",
+    "\n",
+    "    required_cols = ['train_accuracy', 'test_accuracy', 'alpha', 'num_traps', 'ERG_gap']\n",
+    "    for col in required_cols:\n",
+    "        if col in good_df.columns:\n",
+    "            good_df[col] = pd.to_numeric(good_df[col], errors='coerce')\n",
+    "\n",
+    "    if 'train_accuracy' in good_df.columns and 'test_accuracy' in good_df.columns:\n",
+    "        good_df['generalization_gap'] = good_df['train_accuracy'] - good_df['test_accuracy']\n",
+    "\n",
+    "    if good_df.empty:\n",
+    "        print('No well-fit (good) rows found for plotting.')\n",
+    "    elif 'generalization_gap' not in good_df.columns:\n",
+    "        print('Missing required columns: train_accuracy and/or test_accuracy.')\n",
+    "    else:\n",
+    "        dataset_col = 'dataset_uid' if 'dataset_uid' in good_df.columns else None\n",
+    "        if dataset_col is None:\n",
+    "            good_df['dataset_uid'] = 'well_fit_models'\n",
+    "            dataset_col = 'dataset_uid'\n",
+    "\n",
+    "        dataset_order = list(dict.fromkeys(good_df[dataset_col].astype(str).tolist()))\n",
+    "\n",
+    "        palette = plt.cm.tab20(np.linspace(0, 1, max(len(dataset_order), 3)))\n",
+    "        dataset_colors = {dataset: palette[i] for i, dataset in enumerate(dataset_order)}\n",
+    "\n",
+    "        metric_specs = [\n",
+    "            ('alpha', 'alpha'),\n",
+    "            ('num_traps', 'num_traps'),\n",
+    "            ('ERG_gap', 'ERG_gap'),\n",
+    "        ]\n",
+    "\n",
+    "        n_rows = len(dataset_order)\n",
+    "        n_cols = len(metric_specs)\n",
+    "        fig, axes = plt.subplots(n_rows, n_cols, figsize=(5.1 * n_cols, 3.7 * n_rows), squeeze=False)\n",
+    "\n",
+    "        for row_idx, dataset_uid in enumerate(dataset_order):\n",
+    "            row_df = good_df[good_df[dataset_col].astype(str) == dataset_uid].copy()\n",
+    "\n",
+    "            for col_idx, (metric_col, metric_label) in enumerate(metric_specs):\n",
+    "                ax = axes[row_idx, col_idx]\n",
+    "\n",
+    "                if metric_col not in row_df.columns or 'generalization_gap' not in row_df.columns:\n",
+    "                    ax.text(0.5, 0.5, 'Missing required columns', ha='center', va='center', transform=ax.transAxes)\n",
+    "                    ax.set_axis_off()\n",
+    "                    continue\n",
+    "\n",
+    "                cur = row_df.dropna(subset=[metric_col, 'generalization_gap'])\n",
+    "\n",
+    "                if cur.empty:\n",
+    "                    ax.text(0.5, 0.5, f'No data for {dataset_uid}', ha='center', va='center', transform=ax.transAxes)\n",
+    "                    ax.set_xlabel(metric_label)\n",
+    "                    ax.set_ylabel('generalization_gap')\n",
+    "                    ax.grid(alpha=0.2)\n",
+    "                    continue\n",
+    "\n",
+    "                ax.scatter(\n",
+    "                    cur[metric_col],\n",
+    "                    cur['generalization_gap'],\n",
+    "                    color=dataset_colors.get(dataset_uid, '#777777'),\n",
+    "                    edgecolors='black',\n",
+    "                    alpha=0.8,\n",
+    "                    s=42,\n",
+    "                )\n",
+    "                ax.set_xlabel(metric_label)\n",
+    "                ax.set_ylabel('generalization_gap')\n",
+    "                ax.grid(alpha=0.2)\n",
+    "\n",
+    "                if row_idx == 0:\n",
+    "                    ax.set_title(f'{metric_label} vs generalization_gap')\n",
+    "\n",
+    "                if col_idx == 0:\n",
+    "                    ax.text(\n",
+    "                        0.03,\n",
+    "                        0.97,\n",
+    "                        str(dataset_uid),\n",
+    "                        transform=ax.transAxes,\n",
+    "                        va='top',\n",
+    "                        ha='left',\n",
+    "                        fontsize=9,\n",
+    "                        fontweight='bold',\n",
+    "                        color=dataset_colors.get(dataset_uid, '#333333'),\n",
+    "                    )\n",
+    "\n",
+    "        fig.suptitle('Well-fit models: alpha/num_traps/ERG_gap vs generalization_gap by dataset', y=1.002)\n",
+    "        fig.tight_layout()\n",
+    "        plt.show()\n"
+   ]
   }
  ],
  "metadata": {
@@ -5580,4 +5687,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file