diff --git a/docs/contributing.md b/docs/contributing.md
index 45b04a4..98d78ce 100644
--- a/docs/contributing.md
+++ b/docs/contributing.md
@@ -103,6 +103,14 @@ If you updated any of the existing py/ipynb files, you can run `make sync-notebo
 
 `make run-notebooks` will sync, run and save the notebooks, expects an ipython kernel with scikit-mol installed.
 
+If you only want to sync and run a single notebook if you are working on updating one you can adapt the commands from the MakeFile
+
+```bash
+uv run jupytext --set-formats docs//notebooks//ipynb,docs//notebooks//scripts//py:percent --sync docs/notebooks/XX_YourNotebook.ipynb
+uv run ruff format "docs/notebooks/XX_YourNotebook.ipynb"
+uv run jupytext --execute docs/notebooks/XX_YourNotebook.ipynb
+```
+
 ## Documentation
 
 We use [MkDocs](https://www.mkdocs.org/) to host scikit-mol documentation on ReadTheDocs. If you're making some changes to the documentation or just what to see live preview of your docstring you can take a look at rendered documentation.
diff --git a/docs/notebooks/13_applicability_domain.ipynb b/docs/notebooks/13_applicability_domain.ipynb
index e456495..14efd92 100644
--- a/docs/notebooks/13_applicability_domain.ipynb
+++ b/docs/notebooks/13_applicability_domain.ipynb
@@ -18,11 +18,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "40500fae",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-11T09:39:14.229954Z",
+     "iopub.status.busy": "2025-05-11T09:39:14.229779Z",
+     "iopub.status.idle": "2025-05-11T09:39:17.380877Z",
+     "shell.execute_reply": "2025-05-11T09:39:17.380373Z"
+    }
+   },
    "outputs": [],
    "source": [
+    "import os\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "from rdkit import Chem\n",
@@ -49,9 +57,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "79d3b853",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-11T09:39:17.383214Z",
+     "iopub.status.busy": "2025-05-11T09:39:17.382749Z",
+     "iopub.status.idle": "2025-05-11T09:39:17.445075Z",
+     "shell.execute_reply": "2025-05-11T09:39:17.444550Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -63,7 +78,22 @@
    ],
    "source": [
     "# Load the dataset\n",
-    "csv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\"\n",
+    "# Results are better with the full set, but it takes longer to run, so for the notebook documentation we standard use a subset.\n",
+    "# The subset has been filtered to only include nicely predicted compounds, and is thus artificial.\n",
+    "\n",
+    "full_set = False\n",
+    "\n",
+    "if full_set:\n",
+    "    csv_file = \"SLC6A4_active_excape_export.csv\"\n",
+    "    if not os.path.exists(csv_file):\n",
+    "        import urllib.request\n",
+    "\n",
+    "        url = \"https://ndownloader.figshare.com/files/25747817\"\n",
+    "        urllib.request.urlretrieve(url, csv_file)\n",
+    "        percentile = 95\n",
+    "else:\n",
+    "    csv_file = \"../../tests/data/SLC6A4_active_excapedb_subset.csv\"\n",
+    "    percentile = 90\n",
     "data = pd.read_csv(csv_file)\n",
     "\n",
     "# Add RDKit mol objects\n",
@@ -93,51 +123,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "id": "9c89148b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
-      "  return bound(*args, **kwds)\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
-      "  return bound(*args, **kwds)\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
-      "  return bound(*args, **kwds)\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
-      "  warnings.warn(msg, DataConversionWarning)\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
-      "  return bound(*args, **kwds)\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
-      "  warnings.warn(msg, DataConversionWarning)\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
-      "  return bound(*args, **kwds)\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
-      "  warnings.warn(msg, DataConversionWarning)\n"
-     ]
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-11T09:39:17.446860Z",
+     "iopub.status.busy": "2025-05-11T09:39:17.446670Z",
+     "iopub.status.idle": "2025-05-11T09:39:17.835548Z",
+     "shell.execute_reply": "2025-05-11T09:39:17.835023Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# Create pipeline for binary fingerprints\n",
     "binary_fp_pipe = Pipeline(\n",
     "    [\n",
     "        (\"fp\", MorganFingerprintTransformer(fpSize=2048, radius=2)),\n",
-    "        (\"rf\", RandomForestRegressor(n_estimators=100, random_state=42)),\n",
+    "        (\"rf\", RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)),\n",
     "    ]\n",
     ")\n",
     "\n",
@@ -149,7 +151,9 @@
     "abs_errors = np.abs(y_test - y_pred_test)\n",
     "\n",
     "# Create and fit k-NN AD estimator\n",
-    "knn_ad = KNNApplicabilityDomain(n_neighbors=3, distance_metric=\"tanimoto\")\n",
+    "knn_ad = KNNApplicabilityDomain(\n",
+    "    n_neighbors=3, distance_metric=\"tanimoto\", percentile=percentile\n",
+    ")\n",
     "knn_ad.fit(binary_fp_pipe.named_steps[\"fp\"].transform(X_train))\n",
     "\n",
     "# Fit threshold using validation set\n",
@@ -169,13 +173,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "id": "ee7b2f64",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-11T09:39:17.837494Z",
+     "iopub.status.busy": "2025-05-11T09:39:17.837306Z",
+     "iopub.status.idle": "2025-05-11T09:39:18.097264Z",
+     "shell.execute_reply": "2025-05-11T09:39:18.096667Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -187,37 +198,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "95th percentile of errors inside domain: 1.29\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
-      "  return bound(*args, **kwds)\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/anton/projects/scikit-mol/.venv/lib/python3.12/site-packages/sklearn/metrics/pairwise.py:2466: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
-      "  warnings.warn(msg, DataConversionWarning)\n"
-     ]
-    },
-    {
-     "ename": "IndexError",
-     "evalue": "index -1 is out of bounds for axis 0 with size 0",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[8], line 16\u001b[0m\n\u001b[1;32m     13\u001b[0m errors_out \u001b[38;5;241m=\u001b[39m abs_errors[in_domain \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m95th percentile of errors inside domain: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mpercentile(errors_in,\u001b[38;5;250m \u001b[39m\u001b[38;5;241m95\u001b[39m)\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m95th percentile of errors outside domain: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpercentile\u001b[49m\u001b[43m(\u001b[49m\u001b[43merrors_out\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;250;43m \u001b[39;49m\u001b[38;5;241;43m95\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFraction of samples outside domain: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m(in_domain\u001b[38;5;250m \u001b[39m\u001b[38;5;241m==\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mmean()\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m~/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4273\u001b[0m, in \u001b[0;36mpercentile\u001b[0;34m(a, q, axis, out, overwrite_input, method, keepdims, weights, interpolation)\u001b[0m\n\u001b[1;32m   4270\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39many(weights \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m   4271\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWeights must be non-negative.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 4273\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_quantile_unchecked\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   4274\u001b[0m \u001b[43m    \u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverwrite_input\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4550\u001b[0m, in \u001b[0;36m_quantile_unchecked\u001b[0;34m(a, q, axis, out, overwrite_input, method, keepdims, weights)\u001b[0m\n\u001b[1;32m   4541\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_quantile_unchecked\u001b[39m(a,\n\u001b[1;32m   4542\u001b[0m                         q,\n\u001b[1;32m   4543\u001b[0m                         axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4547\u001b[0m                         keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m   4548\u001b[0m                         weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m   4549\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Assumes that q is in [0, 1], and is an ndarray\"\"\"\u001b[39;00m\n\u001b[0;32m-> 4550\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ureduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4551\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_quantile_ureduce_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4552\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4553\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mweights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4554\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4555\u001b[0m \u001b[43m                    \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4556\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4557\u001b[0m \u001b[43m                    \u001b[49m\u001b[43moverwrite_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverwrite_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4558\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:3894\u001b[0m, in \u001b[0;36m_ureduce\u001b[0;34m(a, func, keepdims, **kwargs)\u001b[0m\n\u001b[1;32m   3891\u001b[0m             index_out \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m0\u001b[39m, ) \u001b[38;5;241m*\u001b[39m nd\n\u001b[1;32m   3892\u001b[0m             kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mout\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m out[(\u001b[38;5;28mEllipsis\u001b[39m, ) \u001b[38;5;241m+\u001b[39m index_out]\n\u001b[0;32m-> 3894\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3896\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   3897\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
-      "File \u001b[0;32m~/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4727\u001b[0m, in \u001b[0;36m_quantile_ureduce_func\u001b[0;34m(a, q, weights, axis, out, overwrite_input, method)\u001b[0m\n\u001b[1;32m   4725\u001b[0m         arr \u001b[38;5;241m=\u001b[39m a\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m   4726\u001b[0m         wgt \u001b[38;5;241m=\u001b[39m weights\n\u001b[0;32m-> 4727\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43m_quantile\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4728\u001b[0m \u001b[43m                   \u001b[49m\u001b[43mquantiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4729\u001b[0m \u001b[43m                   \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4730\u001b[0m \u001b[43m                   \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4731\u001b[0m \u001b[43m                   \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4732\u001b[0m \u001b[43m                   \u001b[49m\u001b[43mweights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwgt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4733\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
-      "File \u001b[0;32m~/projects/scikit-mol/.venv/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4849\u001b[0m, in \u001b[0;36m_quantile\u001b[0;34m(arr, quantiles, axis, method, out, weights)\u001b[0m\n\u001b[1;32m   4842\u001b[0m arr\u001b[38;5;241m.\u001b[39mpartition(\n\u001b[1;32m   4843\u001b[0m     np\u001b[38;5;241m.\u001b[39munique(np\u001b[38;5;241m.\u001b[39mconcatenate(([\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m   4844\u001b[0m                               previous_indexes\u001b[38;5;241m.\u001b[39mravel(),\n\u001b[1;32m   4845\u001b[0m                               next_indexes\u001b[38;5;241m.\u001b[39mravel(),\n\u001b[1;32m   4846\u001b[0m                               ))),\n\u001b[1;32m   4847\u001b[0m     axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m   4848\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m supports_nans:\n\u001b[0;32m-> 4849\u001b[0m     slices_having_nans \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39misnan(\u001b[43marr\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[1;32m   4850\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   4851\u001b[0m     slices_having_nans \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "\u001b[0;31mIndexError\u001b[0m: index -1 is out of bounds for axis 0 with size 0"
+      "95th percentile of errors inside domain: 1.31\n",
+      "95th percentile of errors outside domain: 1.17\n",
+      "Fraction of samples outside domain: 0.15\n"
      ]
     }
    ],
@@ -254,455 +237,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "fe4a6819",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
-      "  return bound(*args, **kwds)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-2 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;fp&#x27;, MorganFingerprintTransformer(useCounts=True)),\n",
-       "                (&#x27;pca&#x27;, PCA(n_components=0.9)), (&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;leverage&#x27;, LeverageApplicabilityDomain())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;fp&#x27;, MorganFingerprintTransformer(useCounts=True)),\n",
-       "                (&#x27;pca&#x27;, PCA(n_components=0.9)), (&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;leverage&#x27;, LeverageApplicabilityDomain())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>MorganFingerprintTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-mol.readthedocs.org/en/latest/api/scikit_mol.fingerprints/#scikit_mol.fingerprints.MorganFingerprintTransformer\">?<span>Documentation for MorganFingerprintTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>MorganFingerprintTransformer(useCounts=True)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PCA</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.decomposition.PCA.html\">?<span>Documentation for PCA</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>PCA(n_components=0.9)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LeverageApplicabilityDomain</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>LeverageApplicabilityDomain()</pre></div> </div></div></div></div></div></div>"
-      ],
-      "text/plain": [
-       "Pipeline(steps=[('fp', MorganFingerprintTransformer(useCounts=True)),\n",
-       "                ('pca', PCA(n_components=0.9)), ('scaler', StandardScaler()),\n",
-       "                ('leverage', LeverageApplicabilityDomain())])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-11T09:39:18.099081Z",
+     "iopub.status.busy": "2025-05-11T09:39:18.098853Z",
+     "iopub.status.idle": "2025-05-11T09:39:18.569295Z",
+     "shell.execute_reply": "2025-05-11T09:39:18.568707Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# Create pipeline for count-based fingerprints with PCA\n",
     "count_fp_pipe = Pipeline(\n",
@@ -722,7 +267,7 @@
     "abs_errors = np.abs(y_test - y_pred_test)\n",
     "\n",
     "# Create and fit leverage AD estimator\n",
-    "leverage_ad = LeverageApplicabilityDomain()\n",
+    "leverage_ad = LeverageApplicabilityDomain(percentile=percentile)\n",
     "X_train_transformed = count_fp_pipe.named_steps[\"scaler\"].transform(\n",
     "    count_fp_pipe.named_steps[\"pca\"].transform(\n",
     "        count_fp_pipe.named_steps[\"fp\"].transform(X_train)\n",
@@ -757,24 +302,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "57d73a11",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-11T09:39:18.571392Z",
+     "iopub.status.busy": "2025-05-11T09:39:18.571206Z",
+     "iopub.status.idle": "2025-05-11T09:39:18.710911Z",
+     "shell.execute_reply": "2025-05-11T09:39:18.710436Z"
+    }
+   },
    "outputs": [
     {
-     "name": "stderr",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
-      "  return bound(*args, **kwds)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n",
-      "  return bound(*args, **kwds)\n",
-      "/home/esben/envs/vscode/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
-      "  warnings.warn(\n"
+      "95th percentile of errors inside domain: 1.39\n",
+      "95th percentile of errors outside domain: 1.22\n",
+      "Fraction of samples outside domain: 0.07\n"
      ]
     }
    ],
@@ -810,18 +365,86 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "1d33100d",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-11T09:39:18.712701Z",
+     "iopub.status.busy": "2025-05-11T09:39:18.712517Z",
+     "iopub.status.idle": "2025-05-11T09:39:18.747175Z",
+     "shell.execute_reply": "2025-05-11T09:39:18.746652Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>k-NN Score</th>\n",
+       "      <th>k-NN Status</th>\n",
+       "      <th>Leverage Score</th>\n",
+       "      <th>Leverage Status</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Drug</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Aspirin</th>\n",
+       "      <td>0.813836</td>\n",
+       "      <td>Outside</td>\n",
+       "      <td>0.154726</td>\n",
+       "      <td>Inside</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Viagra</th>\n",
+       "      <td>0.829481</td>\n",
+       "      <td>Outside</td>\n",
+       "      <td>0.374886</td>\n",
+       "      <td>Inside</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Heroin</th>\n",
+       "      <td>0.852061</td>\n",
+       "      <td>Outside</td>\n",
+       "      <td>0.107878</td>\n",
+       "      <td>Inside</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<IPython.core.display.Image object>"
+       "         k-NN Score k-NN Status  Leverage Score Leverage Status\n",
+       "Drug                                                           \n",
+       "Aspirin    0.813836     Outside        0.154726          Inside\n",
+       "Viagra     0.829481     Outside        0.374886          Inside\n",
+       "Heroin     0.852061     Outside        0.107878          Inside"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -887,13 +510,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "3aaf4485",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-11T09:39:18.748976Z",
+     "iopub.status.busy": "2025-05-11T09:39:18.748794Z",
+     "iopub.status.idle": "2025-05-11T09:39:19.086510Z",
+     "shell.execute_reply": "2025-05-11T09:39:19.086029Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x500 with 2 Axes>"
       ]
@@ -954,7 +584,7 @@
     "The famous drugs we tested showed varying degrees of being within the applicability domain, which makes sense given\n",
     "that our training set is focused on SLC6A4 actives, while these drugs have different primary targets.\n",
     "\n",
-    "The error analysis shows that compounds outside the applicability domain tend to have higher prediction errors,\n",
+    "The error analysis shows that compounds outside the applicability domain tend to have higher prediction errors (when using the full set),\n",
     "validating the usefulness of these approaches for identifying potentially unreliable predictions."
    ]
   }
@@ -964,7 +594,7 @@
    "formats": "docs//notebooks//ipynb,docs//notebooks//scripts//py:percent"
   },
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "vscode",
    "language": "python",
    "name": "python3"
   },
@@ -978,7 +608,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.6"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/scripts/13_applicability_domain.py b/docs/notebooks/scripts/13_applicability_domain.py
index 93ace0b..8639f26 100644
--- a/docs/notebooks/scripts/13_applicability_domain.py
+++ b/docs/notebooks/scripts/13_applicability_domain.py
@@ -8,7 +8,7 @@
 #       format_version: '1.3'
 #       jupytext_version: 1.16.6
 #   kernelspec:
-#     display_name: .venv
+#     display_name: vscode
 #     language: python
 #     name: python3
 # ---
@@ -25,6 +25,7 @@
 # First, let's import the necessary libraries and load our dataset:
 
 # %%
+import os
 import numpy as np
 import pandas as pd
 from rdkit import Chem
@@ -45,7 +46,22 @@
 
 # %%
 # Load the dataset
-csv_file = "../../tests/data/SLC6A4_active_excapedb_subset.csv"
+# Results are better with the full set, but it takes longer to run, so for the notebook documentation we standard use a subset.
+# The subset has been filtered to only include nicely predicted compounds, and is thus artificial.
+
+full_set = False
+
+if full_set:
+    csv_file = "SLC6A4_active_excape_export.csv"
+    if not os.path.exists(csv_file):
+        import urllib.request
+
+        url = "https://ndownloader.figshare.com/files/25747817"
+        urllib.request.urlretrieve(url, csv_file)
+        percentile = 95
+else:
+    csv_file = "../../tests/data/SLC6A4_active_excapedb_subset.csv"
+    percentile = 90
 data = pd.read_csv(csv_file)
 
 # Add RDKit mol objects
@@ -72,7 +88,7 @@
 binary_fp_pipe = Pipeline(
     [
         ("fp", MorganFingerprintTransformer(fpSize=2048, radius=2)),
-        ("rf", RandomForestRegressor(n_estimators=100, random_state=42)),
+        ("rf", RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)),
     ]
 )
 
@@ -84,7 +100,7 @@
 abs_errors = np.abs(y_test - y_pred_test)
 
 # Create and fit k-NN AD estimator
-knn_ad = KNNApplicabilityDomain(n_neighbors=3, distance_metric="tanimoto")
+knn_ad = KNNApplicabilityDomain(n_neighbors=3, distance_metric="tanimoto", percentile=percentile)
 knn_ad.fit(binary_fp_pipe.named_steps["fp"].transform(X_train))
 
 # Fit threshold using validation set
@@ -140,7 +156,7 @@
 abs_errors = np.abs(y_test - y_pred_test)
 
 # Create and fit leverage AD estimator
-leverage_ad = LeverageApplicabilityDomain()
+leverage_ad = LeverageApplicabilityDomain(percentile=percentile)
 X_train_transformed = count_fp_pipe.named_steps["scaler"].transform(
     count_fp_pipe.named_steps["pca"].transform(
         count_fp_pipe.named_steps["fp"].transform(X_train)
@@ -291,5 +307,5 @@ def check_drug_applicability(smiles, name):
 # The famous drugs we tested showed varying degrees of being within the applicability domain, which makes sense given
 # that our training set is focused on SLC6A4 actives, while these drugs have different primary targets.
 #
-# The error analysis shows that compounds outside the applicability domain tend to have higher prediction errors,
+# The error analysis shows that compounds outside the applicability domain tend to have higher prediction errors (when using the full set),
 # validating the usefulness of these approaches for identifying potentially unreliable predictions.
diff --git a/scikit_mol/applicability/base.py b/scikit_mol/applicability/base.py
index 8fb0c2b..60b9709 100644
--- a/scikit_mol/applicability/base.py
+++ b/scikit_mol/applicability/base.py
@@ -100,7 +100,7 @@ def __init__(
             "estimator": self,
             "accept_sparse": False,
             "dtype": None,
-            "force_all_finite": True,
+            "ensure_all_finite": True,
             "ensure_2d": True,
         }
 
diff --git a/scikit_mol/applicability/knn.py b/scikit_mol/applicability/knn.py
index e897756..4906f31 100644
--- a/scikit_mol/applicability/knn.py
+++ b/scikit_mol/applicability/knn.py
@@ -146,6 +146,10 @@ def fit(self, X: ArrayLike, y=None) -> "KNNApplicabilityDomain":
             metric=self.distance_metric,
             n_jobs=self.n_jobs,
         )
+
+        if self.distance_metric == "jaccard":
+            X = X.astype(bool)
+
         self.nn_.fit(X)
 
         # Set initial threshold based on training data
@@ -167,6 +171,10 @@ def _transform(self, X: np.ndarray) -> np.ndarray:
             Mean distance to k nearest neighbors. Higher values indicate samples
             further from the training set.
         """
+
+        if self.distance_metric == "jaccard":
+            X = X.astype(bool)
+
         distances, _ = self.nn_.kneighbors(X)
         mean_distances = distances[:, 1:].mean(axis=1)  # Skip first (self) neighbor
         return mean_distances.reshape(-1, 1)