diff --git a/docs/examples/simple-mag/mag-distribution.png b/docs/examples/simple-mag/mag-distribution.png
index a724ab1..c3246cc 100644
Binary files a/docs/examples/simple-mag/mag-distribution.png and b/docs/examples/simple-mag/mag-distribution.png differ
diff --git a/docs/examples/simple-mag/train.ipynb b/docs/examples/simple-mag/train.ipynb
index cb4f991..75ed417 100644
--- a/docs/examples/simple-mag/train.ipynb
+++ b/docs/examples/simple-mag/train.ipynb
@@ -19,15 +19,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "84000it [00:09, 8647.60it/s] \n",
-      "22399it [00:03, 6368.63it/s]\n"
+      "100%|██████████| 84000/84000 [00:08<00:00, 9514.74it/s] \n",
+      "100%|██████████| 22399/22399 [00:02<00:00, 7728.92it/s] \n"
      ]
     }
    ],
    "source": [
     "import json\n",
     "from typing import Tuple\n",
-    "from great_ai.utilities import clean, parallel_map\n",
+    "from great_ai.utilities import clean, simple_parallel_map\n",
     "from tqdm.cli import tqdm\n",
     "\n",
     "\n",
@@ -37,15 +37,15 @@
     "    return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"])\n",
     "\n",
     "\n",
-    "with open(\"mag/train.txt\", encoding=\"utf-8\") as f:\n",
-    "    training_data = list(tqdm(parallel_map(preprocess, f.readlines())))\n",
+    "with open(\"../../tutorial/data/train.txt\", encoding=\"utf-8\") as f:\n",
+    "    training_data = simple_parallel_map(preprocess, f.readlines())\n",
     "\n",
     "X_train = [d[0] for d in training_data]\n",
     "y_train = [d[1] for d in training_data]\n",
     "\n",
     "\n",
-    "with open(\"mag/test.txt\", encoding=\"utf-8\") as f:\n",
-    "    test_data = list(tqdm(parallel_map(preprocess, f.readlines())))\n",
+    "with open(\"../../tutorial/data/test.txt\", encoding=\"utf-8\") as f:\n",
+    "    test_data = simple_parallel_map(preprocess, f.readlines())\n",
     "\n",
     "X_test = [d[0] for d in test_data]\n",
     "y_test = [d[1] for d in test_data]"
@@ -58,910 +58,35 @@
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",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "import pandas as pd\n",
     "from collections import Counter\n",
-    "import plotly.express as px\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
+    "domains, counts = zip(*Counter(y_train).most_common())\n",
     "\n",
-    "df = pd.DataFrame(Counter(y_train).most_common(), columns=[\"domain\", \"count\"])\n",
-    "px.bar(df, \"domain\", \"count\", width=1200, height=400).show()"
+    "# Configure matplotlib to have nice, high-resolution charts\n",
+    "%matplotlib inline\n",
+    "plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
+    "plt.rcParams[\"font.size\"] = 18\n",
+    "plt.rcParams[\"figure.figsize\"] = (8, 5)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "plt.xticks(rotation=90)\n",
+    "ax.bar(domains, counts)\n",
+    "ax.set_ylabel(\"Count\")\n",
+    "fig.tight_layout()\n",
+    "fig.savefig(\"mag-distribution.png\", dpi=500)\n",
+    "None"
    ]
   },
   {
@@ -1037,10 +162,10 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>1.227354</td>\n",
-       "      <td>0.011710</td>\n",
-       "      <td>0.652844</td>\n",
-       "      <td>0.040511</td>\n",
+       "      <td>1.410234</td>\n",
+       "      <td>0.165616</td>\n",
+       "      <td>0.616949</td>\n",
+       "      <td>0.017791</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -1055,10 +180,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
-       "      <td>1.279783</td>\n",
-       "      <td>0.037969</td>\n",
-       "      <td>0.634761</td>\n",
-       "      <td>0.068606</td>\n",
+       "      <td>1.356129</td>\n",
+       "      <td>0.033539</td>\n",
+       "      <td>0.799676</td>\n",
+       "      <td>0.077166</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -1073,10 +198,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>1.332854</td>\n",
-       "      <td>0.171405</td>\n",
-       "      <td>0.743611</td>\n",
-       "      <td>0.109792</td>\n",
+       "      <td>1.191132</td>\n",
+       "      <td>0.046271</td>\n",
+       "      <td>0.633976</td>\n",
+       "      <td>0.009344</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -1091,10 +216,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
-       "      <td>1.253222</td>\n",
-       "      <td>0.072134</td>\n",
-       "      <td>0.612344</td>\n",
-       "      <td>0.016771</td>\n",
+       "      <td>1.206345</td>\n",
+       "      <td>0.121633</td>\n",
+       "      <td>0.494508</td>\n",
+       "      <td>0.101456</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -1109,10 +234,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1.472035</td>\n",
-       "      <td>0.080849</td>\n",
-       "      <td>0.654935</td>\n",
-       "      <td>0.044302</td>\n",
+       "      <td>1.434749</td>\n",
+       "      <td>0.077467</td>\n",
+       "      <td>0.665522</td>\n",
+       "      <td>0.042225</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -1127,10 +252,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.380641</td>\n",
-       "      <td>0.054306</td>\n",
-       "      <td>0.739966</td>\n",
-       "      <td>0.053385</td>\n",
+       "      <td>1.338873</td>\n",
+       "      <td>0.100248</td>\n",
+       "      <td>0.791040</td>\n",
+       "      <td>0.061094</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -1145,10 +270,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>1.284987</td>\n",
-       "      <td>0.113903</td>\n",
-       "      <td>0.696876</td>\n",
-       "      <td>0.003757</td>\n",
+       "      <td>1.429841</td>\n",
+       "      <td>0.071771</td>\n",
+       "      <td>0.868423</td>\n",
+       "      <td>0.097744</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -1163,10 +288,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>1.291148</td>\n",
-       "      <td>0.101837</td>\n",
-       "      <td>0.686561</td>\n",
-       "      <td>0.083989</td>\n",
+       "      <td>1.320994</td>\n",
+       "      <td>0.115772</td>\n",
+       "      <td>0.708544</td>\n",
+       "      <td>0.028758</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -1181,10 +306,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>1.268873</td>\n",
-       "      <td>0.042412</td>\n",
-       "      <td>0.645649</td>\n",
-       "      <td>0.022738</td>\n",
+       "      <td>1.430184</td>\n",
+       "      <td>0.068179</td>\n",
+       "      <td>0.667534</td>\n",
+       "      <td>0.045122</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -1199,10 +324,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>1.147120</td>\n",
-       "      <td>0.009335</td>\n",
-       "      <td>0.599006</td>\n",
-       "      <td>0.045637</td>\n",
+       "      <td>1.230683</td>\n",
+       "      <td>0.097298</td>\n",
+       "      <td>0.657511</td>\n",
+       "      <td>0.015534</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -1217,10 +342,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1.186988</td>\n",
-       "      <td>0.057330</td>\n",
-       "      <td>0.642233</td>\n",
-       "      <td>0.078248</td>\n",
+       "      <td>1.247198</td>\n",
+       "      <td>0.061587</td>\n",
+       "      <td>0.684918</td>\n",
+       "      <td>0.085735</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -1235,10 +360,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.413231</td>\n",
-       "      <td>0.153288</td>\n",
-       "      <td>0.765960</td>\n",
-       "      <td>0.099535</td>\n",
+       "      <td>1.367997</td>\n",
+       "      <td>0.161019</td>\n",
+       "      <td>0.720563</td>\n",
+       "      <td>0.047895</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -1253,10 +378,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>1.193013</td>\n",
-       "      <td>0.079000</td>\n",
-       "      <td>0.691768</td>\n",
-       "      <td>0.027448</td>\n",
+       "      <td>1.276051</td>\n",
+       "      <td>0.053059</td>\n",
+       "      <td>0.670412</td>\n",
+       "      <td>0.055486</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -1271,10 +396,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
-       "      <td>1.043618</td>\n",
-       "      <td>0.098733</td>\n",
-       "      <td>0.450375</td>\n",
-       "      <td>0.061660</td>\n",
+       "      <td>1.098513</td>\n",
+       "      <td>0.062989</td>\n",
+       "      <td>0.454028</td>\n",
+       "      <td>0.049762</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -1289,10 +414,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>1.301459</td>\n",
-       "      <td>0.062143</td>\n",
-       "      <td>0.660748</td>\n",
-       "      <td>0.056054</td>\n",
+       "      <td>1.246671</td>\n",
+       "      <td>0.070173</td>\n",
+       "      <td>0.678239</td>\n",
+       "      <td>0.061924</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -1307,10 +432,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>1.433934</td>\n",
-       "      <td>0.155240</td>\n",
-       "      <td>0.636608</td>\n",
-       "      <td>0.024064</td>\n",
+       "      <td>1.335585</td>\n",
+       "      <td>0.055454</td>\n",
+       "      <td>0.714089</td>\n",
+       "      <td>0.031715</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -1325,10 +450,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>1.325535</td>\n",
-       "      <td>0.073539</td>\n",
-       "      <td>0.672542</td>\n",
-       "      <td>0.085835</td>\n",
+       "      <td>1.433829</td>\n",
+       "      <td>0.102381</td>\n",
+       "      <td>0.584196</td>\n",
+       "      <td>0.020291</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -1343,10 +468,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>1.237677</td>\n",
-       "      <td>0.070063</td>\n",
-       "      <td>0.651091</td>\n",
-       "      <td>0.102538</td>\n",
+       "      <td>1.259510</td>\n",
+       "      <td>0.128875</td>\n",
+       "      <td>0.561018</td>\n",
+       "      <td>0.006103</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -1361,10 +486,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1.394873</td>\n",
-       "      <td>0.105286</td>\n",
-       "      <td>0.637073</td>\n",
-       "      <td>0.041708</td>\n",
+       "      <td>1.369987</td>\n",
+       "      <td>0.103349</td>\n",
+       "      <td>0.699614</td>\n",
+       "      <td>0.033642</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -1379,10 +504,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>1.270732</td>\n",
-       "      <td>0.013414</td>\n",
-       "      <td>0.620984</td>\n",
-       "      <td>0.025393</td>\n",
+       "      <td>1.292009</td>\n",
+       "      <td>0.083116</td>\n",
+       "      <td>0.621565</td>\n",
+       "      <td>0.058068</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -1397,10 +522,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
-       "      <td>1.177219</td>\n",
-       "      <td>0.039633</td>\n",
-       "      <td>0.586308</td>\n",
-       "      <td>0.044247</td>\n",
+       "      <td>1.158813</td>\n",
+       "      <td>0.039281</td>\n",
+       "      <td>0.524616</td>\n",
+       "      <td>0.016054</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -1415,10 +540,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>1.165583</td>\n",
-       "      <td>0.046999</td>\n",
-       "      <td>0.603675</td>\n",
-       "      <td>0.031774</td>\n",
+       "      <td>1.302560</td>\n",
+       "      <td>0.095545</td>\n",
+       "      <td>0.709228</td>\n",
+       "      <td>0.144463</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -1433,10 +558,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
-       "      <td>0.907388</td>\n",
-       "      <td>0.121543</td>\n",
-       "      <td>0.354655</td>\n",
-       "      <td>0.023775</td>\n",
+       "      <td>1.093018</td>\n",
+       "      <td>0.033991</td>\n",
+       "      <td>0.331076</td>\n",
+       "      <td>0.005935</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -1451,10 +576,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>1.210845</td>\n",
-       "      <td>0.041339</td>\n",
-       "      <td>0.653606</td>\n",
-       "      <td>0.028117</td>\n",
+       "      <td>1.534622</td>\n",
+       "      <td>0.081777</td>\n",
+       "      <td>0.649834</td>\n",
+       "      <td>0.033149</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -1473,30 +598,30 @@
       ],
       "text/plain": [
        "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
-       "12       1.227354      0.011710         0.652844        0.040511   \n",
-       "15       1.279783      0.037969         0.634761        0.068606   \n",
-       "18       1.332854      0.171405         0.743611        0.109792   \n",
-       "21       1.253222      0.072134         0.612344        0.016771   \n",
-       "3        1.472035      0.080849         0.654935        0.044302   \n",
-       "0        1.380641      0.054306         0.739966        0.053385   \n",
-       "9        1.284987      0.113903         0.696876        0.003757   \n",
-       "6        1.291148      0.101837         0.686561        0.083989   \n",
-       "13       1.268873      0.042412         0.645649        0.022738   \n",
-       "16       1.147120      0.009335         0.599006        0.045637   \n",
-       "4        1.186988      0.057330         0.642233        0.078248   \n",
-       "1        1.413231      0.153288         0.765960        0.099535   \n",
-       "19       1.193013      0.079000         0.691768        0.027448   \n",
-       "22       1.043618      0.098733         0.450375        0.061660   \n",
-       "7        1.301459      0.062143         0.660748        0.056054   \n",
-       "10       1.433934      0.155240         0.636608        0.024064   \n",
-       "14       1.325535      0.073539         0.672542        0.085835   \n",
-       "17       1.237677      0.070063         0.651091        0.102538   \n",
-       "2        1.394873      0.105286         0.637073        0.041708   \n",
-       "5        1.270732      0.013414         0.620984        0.025393   \n",
-       "20       1.177219      0.039633         0.586308        0.044247   \n",
-       "8        1.165583      0.046999         0.603675        0.031774   \n",
-       "23       0.907388      0.121543         0.354655        0.023775   \n",
-       "11       1.210845      0.041339         0.653606        0.028117   \n",
+       "12       1.410234      0.165616         0.616949        0.017791   \n",
+       "15       1.356129      0.033539         0.799676        0.077166   \n",
+       "18       1.191132      0.046271         0.633976        0.009344   \n",
+       "21       1.206345      0.121633         0.494508        0.101456   \n",
+       "3        1.434749      0.077467         0.665522        0.042225   \n",
+       "0        1.338873      0.100248         0.791040        0.061094   \n",
+       "9        1.429841      0.071771         0.868423        0.097744   \n",
+       "6        1.320994      0.115772         0.708544        0.028758   \n",
+       "13       1.430184      0.068179         0.667534        0.045122   \n",
+       "16       1.230683      0.097298         0.657511        0.015534   \n",
+       "4        1.247198      0.061587         0.684918        0.085735   \n",
+       "1        1.367997      0.161019         0.720563        0.047895   \n",
+       "19       1.276051      0.053059         0.670412        0.055486   \n",
+       "22       1.098513      0.062989         0.454028        0.049762   \n",
+       "7        1.246671      0.070173         0.678239        0.061924   \n",
+       "10       1.335585      0.055454         0.714089        0.031715   \n",
+       "14       1.433829      0.102381         0.584196        0.020291   \n",
+       "17       1.259510      0.128875         0.561018        0.006103   \n",
+       "2        1.369987      0.103349         0.699614        0.033642   \n",
+       "5        1.292009      0.083116         0.621565        0.058068   \n",
+       "20       1.158813      0.039281         0.524616        0.016054   \n",
+       "8        1.302560      0.095545         0.709228        0.144463   \n",
+       "23       1.093018      0.033991         0.331076        0.005935   \n",
+       "11       1.534622      0.081777         0.649834        0.033149   \n",
        "\n",
        "   param_classifier__alpha param_classifier__fit_prior  \\\n",
        "12                       1                        True   \n",
@@ -1636,6 +761,7 @@
    ],
    "source": [
     "from sklearn.model_selection import GridSearchCV\n",
+    "import pandas as pd\n",
     "\n",
     "optimisation_pipeline = GridSearchCV(\n",
     "    create_pipeline(),\n",
diff --git a/docs/examples/simple/data.ipynb b/docs/examples/simple/data.ipynb
index 0835917..30c437b 100644
--- a/docs/examples/simple/data.ipynb
+++ b/docs/examples/simple/data.ipynb
@@ -87,7 +87,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 4/4 [04:42<00:00, 70.62s/it] \n"
+      "100%|██████████| 4/4 [04:22<00:00, 65.51s/it] \n"
      ]
     }
    ],
@@ -103,10 +103,11 @@
     "    unchunk,\n",
     ")\n",
     "\n",
+    "\n",
     "def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]:\n",
     "    response = urllib.request.urlopen(\n",
     "        f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\"\n",
-    "        \"open-corpus/2022-02-01/{chunk_key}\"\n",
+    "        f\"open-corpus/2022-02-01/{chunk_key}\"\n",
     "    )  # a gzipped JSON Lines file\n",
     "\n",
     "    decompressed = gzip.decompress(response.read())\n",
@@ -165,9 +166,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[38;5;226mEnvironment variable ENVIRONMENT is not set, defaulting to development mode ‼️\u001b[0m\n",
+      "\u001b[38;5;226mCannot find credentials files, defaulting to using ParallelTinyDbDriver\u001b[0m\n",
+      "\u001b[38;5;226mThe selected tracing database (ParallelTinyDbDriver) is not recommended for production\u001b[0m\n",
+      "\u001b[38;5;226mCannot find credentials files, defaulting to using LargeFileLocal\u001b[0m\n",
+      "\u001b[38;5;39mGreatAI (v0.1.6): configured ✅\u001b[0m\n",
+      "\u001b[38;5;39m  🔩 tracing_database: ParallelTinyDbDriver\u001b[0m\n",
+      "\u001b[38;5;39m  🔩 large_file_implementation: LargeFileLocal\u001b[0m\n",
+      "\u001b[38;5;39m  🔩 is_production: False\u001b[0m\n",
+      "\u001b[38;5;39m  🔩 should_log_exception_stack: True\u001b[0m\n",
+      "\u001b[38;5;39m  🔩 prediction_cache_size: 512\u001b[0m\n",
+      "\u001b[38;5;39m  🔩 dashboard_table_size: 50\u001b[0m\n",
+      "\u001b[38;5;226mYou still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n",
+      "\u001b[38;5;226m> Find out more at https://se-ml.github.io/practices\u001b[0m\n"
+     ]
+    }
+   ],
    "source": [
     "from great_ai import add_ground_truth\n",
     "\n",
diff --git a/docs/examples/simple/ss-distribution.png b/docs/examples/simple/ss-distribution.png
new file mode 100644
index 0000000..cad0d3e
Binary files /dev/null and b/docs/examples/simple/ss-distribution.png differ
diff --git a/docs/examples/simple/stacked-bars.ipynb b/docs/examples/simple/stacked-bars.ipynb
new file mode 100644
index 0000000..a85a072
--- /dev/null
+++ b/docs/examples/simple/stacked-bars.ipynb
@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[38;5;226mEnvironment variable ENVIRONMENT is not set, defaulting to development mode ‼️\u001b[0m\n",
+      "\u001b[38;5;226mCannot find credentials files, defaulting to using ParallelTinyDbDriver\u001b[0m\n",
+      "\u001b[38;5;226mThe selected tracing database (ParallelTinyDbDriver) is not recommended for production\u001b[0m\n",
+      "\u001b[38;5;226mCannot find credentials files, defaulting to using LargeFileLocal\u001b[0m\n",
+      "\u001b[38;5;39mGreatAI (v0.1.6): configured ✅\u001b[0m\n",
+      "\u001b[38;5;39m  🔩 tracing_database: ParallelTinyDbDriver\u001b[0m\n",
+      "\u001b[38;5;39m  🔩 large_file_implementation: LargeFileLocal\u001b[0m\n",
+      "\u001b[38;5;39m  🔩 is_production: False\u001b[0m\n",
+      "\u001b[38;5;39m  🔩 should_log_exception_stack: True\u001b[0m\n",
+      "\u001b[38;5;39m  🔩 prediction_cache_size: 512\u001b[0m\n",
+      "\u001b[38;5;39m  🔩 dashboard_table_size: 50\u001b[0m\n",
+      "\u001b[38;5;226mYou still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n",
+      "\u001b[38;5;226m> Find out more at https://se-ml.github.io/practices\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "from great_ai import query_ground_truth\n",
+    "\n",
+    "data = query_ground_truth(\"train\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from collections import Counter\n",
+    "import matplotlib.pyplot as plt\n",
+    "from great_ai.utilities import unique\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "first = Counter(d.feedback[0] for d in data if len(d.feedback) > 0)\n",
+    "\n",
+    "second = Counter(d.feedback[1] for d in data if len(d.feedback) > 1)\n",
+    "\n",
+    "third = Counter(d.feedback[2] for d in data if len(d.feedback) > 2)\n",
+    "\n",
+    "domains = sorted(unique(domain for d in data for domain in d.feedback))\n",
+    "\n",
+    "first = [first[d] / len(data) for d in domains]\n",
+    "\n",
+    "second = [second[d] / len(data) for d in domains]\n",
+    "\n",
+    "third = [third[d] / len(data) for d in domains]\n",
+    "\n",
+    "# Configure matplotlib to have nice, high-resolution charts\n",
+    "%matplotlib inline\n",
+    "plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
+    "plt.rcParams[\"font.size\"] = 20\n",
+    "plt.rcParams[\"figure.figsize\"] = (14, 10)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "plt.xticks(rotation=90)\n",
+    "\n",
+    "ax.bar(domains, first, label=\"Primary domain\")\n",
+    "ax.bar(domains, second, label=\"Secondary domain\", bottom=first)\n",
+    "ax.bar(domains, third, label=\"Tertiary domain\", bottom=np.add(first, second))\n",
+    "ax.legend()\n",
+    "ax.set_ylabel(\"Ratio of all publications\")\n",
+    "fig.tight_layout()\n",
+    "fig.savefig(\"ss-distribution.png\", dpi=500)\n",
+    "None"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4 ('.env': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "02dd6d3afbfa9fbbe1037d64ad9014965528a1ccad21929d6e72f466389a68ad"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/examples/simple/train.ipynb b/docs/examples/simple/train.ipynb
index 41577cf..d2d94c5 100644
--- a/docs/examples/simple/train.ipynb
+++ b/docs/examples/simple/train.ipynb
@@ -29,7 +29,7 @@
       "\u001b[38;5;226mCannot find credentials files, defaulting to using ParallelTinyDbDriver\u001b[0m\n",
       "\u001b[38;5;226mThe selected tracing database (ParallelTinyDbDriver) is not recommended for production\u001b[0m\n",
       "\u001b[38;5;226mCannot find credentials files, defaulting to using LargeFileLocal\u001b[0m\n",
-      "\u001b[38;5;39mGreatAI (v0.1.4): configured ✅\u001b[0m\n",
+      "\u001b[38;5;39mGreatAI (v0.1.6): configured ✅\u001b[0m\n",
       "\u001b[38;5;39m  🔩 tracing_database: ParallelTinyDbDriver\u001b[0m\n",
       "\u001b[38;5;39m  🔩 large_file_implementation: LargeFileLocal\u001b[0m\n",
       "\u001b[38;5;39m  🔩 is_production: False\u001b[0m\n",
@@ -51,12 +51,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1440x360 with 1 Axes>"
       ]
@@ -122,641 +122,15 @@
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>mean_fit_time</th>\n",
-       "      <th>std_fit_time</th>\n",
-       "      <th>mean_score_time</th>\n",
-       "      <th>std_score_time</th>\n",
-       "      <th>param_classifier__alpha</th>\n",
-       "      <th>param_classifier__fit_prior</th>\n",
-       "      <th>param_vectorizer__max_df</th>\n",
-       "      <th>param_vectorizer__min_df</th>\n",
-       "      <th>params</th>\n",
-       "      <th>split0_test_score</th>\n",
-       "      <th>split1_test_score</th>\n",
-       "      <th>split2_test_score</th>\n",
-       "      <th>mean_test_score</th>\n",
-       "      <th>std_test_score</th>\n",
-       "      <th>rank_test_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>9.447799</td>\n",
-       "      <td>0.785535</td>\n",
-       "      <td>4.260490</td>\n",
-       "      <td>0.180851</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>20</td>\n",
-       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
-       "      <td>0.517618</td>\n",
-       "      <td>0.518688</td>\n",
-       "      <td>0.513803</td>\n",
-       "      <td>0.516703</td>\n",
-       "      <td>0.002096</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>9.750011</td>\n",
-       "      <td>0.671769</td>\n",
-       "      <td>4.791153</td>\n",
-       "      <td>0.544862</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>20</td>\n",
-       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
-       "      <td>0.513254</td>\n",
-       "      <td>0.517940</td>\n",
-       "      <td>0.509116</td>\n",
-       "      <td>0.513437</td>\n",
-       "      <td>0.003605</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>9.398448</td>\n",
-       "      <td>0.656403</td>\n",
-       "      <td>5.194527</td>\n",
-       "      <td>0.303382</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>100</td>\n",
-       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
-       "      <td>0.508306</td>\n",
-       "      <td>0.502244</td>\n",
-       "      <td>0.501839</td>\n",
-       "      <td>0.504129</td>\n",
-       "      <td>0.002958</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>9.285461</td>\n",
-       "      <td>0.869226</td>\n",
-       "      <td>5.030563</td>\n",
-       "      <td>0.305472</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>100</td>\n",
-       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
-       "      <td>0.504070</td>\n",
-       "      <td>0.502116</td>\n",
-       "      <td>0.500603</td>\n",
-       "      <td>0.502263</td>\n",
-       "      <td>0.001419</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>9.662259</td>\n",
-       "      <td>0.234167</td>\n",
-       "      <td>4.854762</td>\n",
-       "      <td>0.126622</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>20</td>\n",
-       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
-       "      <td>0.492205</td>\n",
-       "      <td>0.504466</td>\n",
-       "      <td>0.495489</td>\n",
-       "      <td>0.497387</td>\n",
-       "      <td>0.005182</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>9.024540</td>\n",
-       "      <td>0.469833</td>\n",
-       "      <td>3.235823</td>\n",
-       "      <td>0.173482</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>100</td>\n",
-       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
-       "      <td>0.501203</td>\n",
-       "      <td>0.493465</td>\n",
-       "      <td>0.492361</td>\n",
-       "      <td>0.495676</td>\n",
-       "      <td>0.003934</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>9.304917</td>\n",
-       "      <td>0.192977</td>\n",
-       "      <td>4.370798</td>\n",
-       "      <td>0.255480</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>100</td>\n",
-       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
-       "      <td>0.498869</td>\n",
-       "      <td>0.494357</td>\n",
-       "      <td>0.492431</td>\n",
-       "      <td>0.495219</td>\n",
-       "      <td>0.002698</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>9.642570</td>\n",
-       "      <td>0.438559</td>\n",
-       "      <td>3.332855</td>\n",
-       "      <td>0.435930</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>20</td>\n",
-       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
-       "      <td>0.479585</td>\n",
-       "      <td>0.496964</td>\n",
-       "      <td>0.485209</td>\n",
-       "      <td>0.487253</td>\n",
-       "      <td>0.007241</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>10.047471</td>\n",
-       "      <td>0.473215</td>\n",
-       "      <td>4.358744</td>\n",
-       "      <td>0.051892</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>5</td>\n",
-       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
-       "      <td>0.472578</td>\n",
-       "      <td>0.486362</td>\n",
-       "      <td>0.481865</td>\n",
-       "      <td>0.480268</td>\n",
-       "      <td>0.005739</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>10.287111</td>\n",
-       "      <td>0.349778</td>\n",
-       "      <td>4.908435</td>\n",
-       "      <td>0.099558</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>100</td>\n",
-       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
-       "      <td>0.474821</td>\n",
-       "      <td>0.474690</td>\n",
-       "      <td>0.478096</td>\n",
-       "      <td>0.475869</td>\n",
-       "      <td>0.001576</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>10.997282</td>\n",
-       "      <td>0.851917</td>\n",
-       "      <td>6.131619</td>\n",
-       "      <td>0.274400</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>100</td>\n",
-       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
-       "      <td>0.476218</td>\n",
-       "      <td>0.471377</td>\n",
-       "      <td>0.478548</td>\n",
-       "      <td>0.475381</td>\n",
-       "      <td>0.002987</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>9.147621</td>\n",
-       "      <td>0.290876</td>\n",
-       "      <td>5.392678</td>\n",
-       "      <td>0.390403</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
-       "      <td>0.458994</td>\n",
-       "      <td>0.475008</td>\n",
-       "      <td>0.469998</td>\n",
-       "      <td>0.468000</td>\n",
-       "      <td>0.006689</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10.299674</td>\n",
-       "      <td>0.486001</td>\n",
-       "      <td>5.776907</td>\n",
-       "      <td>0.462777</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>20</td>\n",
-       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
-       "      <td>0.461036</td>\n",
-       "      <td>0.466296</td>\n",
-       "      <td>0.467280</td>\n",
-       "      <td>0.464871</td>\n",
-       "      <td>0.002741</td>\n",
-       "      <td>13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>10.771393</td>\n",
-       "      <td>0.330218</td>\n",
-       "      <td>4.961415</td>\n",
-       "      <td>0.454726</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>20</td>\n",
-       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
-       "      <td>0.454689</td>\n",
-       "      <td>0.463340</td>\n",
-       "      <td>0.457743</td>\n",
-       "      <td>0.458591</td>\n",
-       "      <td>0.003582</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>9.969123</td>\n",
-       "      <td>0.715883</td>\n",
-       "      <td>4.382175</td>\n",
-       "      <td>0.216170</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>100</td>\n",
-       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
-       "      <td>0.444039</td>\n",
-       "      <td>0.449822</td>\n",
-       "      <td>0.450524</td>\n",
-       "      <td>0.448128</td>\n",
-       "      <td>0.002906</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>9.439775</td>\n",
-       "      <td>1.333323</td>\n",
-       "      <td>4.914944</td>\n",
-       "      <td>0.292653</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>100</td>\n",
-       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
-       "      <td>0.440663</td>\n",
-       "      <td>0.448419</td>\n",
-       "      <td>0.443441</td>\n",
-       "      <td>0.444174</td>\n",
-       "      <td>0.003209</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>10.025776</td>\n",
-       "      <td>0.198347</td>\n",
-       "      <td>5.375965</td>\n",
-       "      <td>0.220059</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>5</td>\n",
-       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
-       "      <td>0.418284</td>\n",
-       "      <td>0.428063</td>\n",
-       "      <td>0.428696</td>\n",
-       "      <td>0.425014</td>\n",
-       "      <td>0.004766</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>9.251913</td>\n",
-       "      <td>0.441015</td>\n",
-       "      <td>5.085037</td>\n",
-       "      <td>0.224299</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>20</td>\n",
-       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
-       "      <td>0.400471</td>\n",
-       "      <td>0.406275</td>\n",
-       "      <td>0.406521</td>\n",
-       "      <td>0.404422</td>\n",
-       "      <td>0.002796</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>9.173751</td>\n",
-       "      <td>0.112242</td>\n",
-       "      <td>4.271786</td>\n",
-       "      <td>0.293579</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
-       "      <td>0.393559</td>\n",
-       "      <td>0.402869</td>\n",
-       "      <td>0.406431</td>\n",
-       "      <td>0.400953</td>\n",
-       "      <td>0.005427</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>9.683013</td>\n",
-       "      <td>0.600089</td>\n",
-       "      <td>4.924559</td>\n",
-       "      <td>0.321389</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>20</td>\n",
-       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
-       "      <td>0.387535</td>\n",
-       "      <td>0.392661</td>\n",
-       "      <td>0.393992</td>\n",
-       "      <td>0.391396</td>\n",
-       "      <td>0.002784</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>9.835260</td>\n",
-       "      <td>0.220524</td>\n",
-       "      <td>6.034349</td>\n",
-       "      <td>0.586497</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>5</td>\n",
-       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
-       "      <td>0.379471</td>\n",
-       "      <td>0.381348</td>\n",
-       "      <td>0.386078</td>\n",
-       "      <td>0.382299</td>\n",
-       "      <td>0.002780</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11.905621</td>\n",
-       "      <td>0.329256</td>\n",
-       "      <td>5.571229</td>\n",
-       "      <td>0.541937</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
-       "      <td>0.368234</td>\n",
-       "      <td>0.367599</td>\n",
-       "      <td>0.372849</td>\n",
-       "      <td>0.369561</td>\n",
-       "      <td>0.002339</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>10.378068</td>\n",
-       "      <td>0.227200</td>\n",
-       "      <td>5.247946</td>\n",
-       "      <td>0.126687</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>5</td>\n",
-       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
-       "      <td>0.278087</td>\n",
-       "      <td>0.278157</td>\n",
-       "      <td>0.278143</td>\n",
-       "      <td>0.278129</td>\n",
-       "      <td>0.000030</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>10.375661</td>\n",
-       "      <td>0.139826</td>\n",
-       "      <td>5.239350</td>\n",
-       "      <td>0.298795</td>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
-       "      <td>0.259347</td>\n",
-       "      <td>0.262720</td>\n",
-       "      <td>0.258716</td>\n",
-       "      <td>0.260261</td>\n",
-       "      <td>0.001758</td>\n",
-       "      <td>24</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
-       "7        9.447799      0.785535         4.260490        0.180851   \n",
-       "10       9.750011      0.671769         4.791153        0.544862   \n",
-       "11       9.398448      0.656403         5.194527        0.303382   \n",
-       "8        9.285461      0.869226         5.030563        0.305472   \n",
-       "19       9.662259      0.234167         4.854762        0.126622   \n",
-       "23       9.024540      0.469833         3.235823        0.173482   \n",
-       "20       9.304917      0.192977         4.370798        0.255480   \n",
-       "22       9.642570      0.438559         3.332855        0.435930   \n",
-       "6       10.047471      0.473215         4.358744        0.051892   \n",
-       "5       10.287111      0.349778         4.908435        0.099558   \n",
-       "2       10.997282      0.851917         6.131619        0.274400   \n",
-       "9        9.147621      0.290876         5.392678        0.390403   \n",
-       "1       10.299674      0.486001         5.776907        0.462777   \n",
-       "4       10.771393      0.330218         4.961415        0.454726   \n",
-       "14       9.969123      0.715883         4.382175        0.216170   \n",
-       "17       9.439775      1.333323         4.914944        0.292653   \n",
-       "18      10.025776      0.198347         5.375965        0.220059   \n",
-       "13       9.251913      0.441015         5.085037        0.224299   \n",
-       "21       9.173751      0.112242         4.271786        0.293579   \n",
-       "16       9.683013      0.600089         4.924559        0.321389   \n",
-       "0        9.835260      0.220524         6.034349        0.586497   \n",
-       "3       11.905621      0.329256         5.571229        0.541937   \n",
-       "12      10.378068      0.227200         5.247946        0.126687   \n",
-       "15      10.375661      0.139826         5.239350        0.298795   \n",
-       "\n",
-       "   param_classifier__alpha param_classifier__fit_prior  \\\n",
-       "7                      0.5                       False   \n",
-       "10                     0.5                       False   \n",
-       "11                     0.5                       False   \n",
-       "8                      0.5                       False   \n",
-       "19                       1                       False   \n",
-       "23                       1                       False   \n",
-       "20                       1                       False   \n",
-       "22                       1                       False   \n",
-       "6                      0.5                       False   \n",
-       "5                      0.5                        True   \n",
-       "2                      0.5                        True   \n",
-       "9                      0.5                       False   \n",
-       "1                      0.5                        True   \n",
-       "4                      0.5                        True   \n",
-       "14                       1                        True   \n",
-       "17                       1                        True   \n",
-       "18                       1                       False   \n",
-       "13                       1                        True   \n",
-       "21                       1                       False   \n",
-       "16                       1                        True   \n",
-       "0                      0.5                        True   \n",
-       "3                      0.5                        True   \n",
-       "12                       1                        True   \n",
-       "15                       1                        True   \n",
-       "\n",
-       "   param_vectorizer__max_df param_vectorizer__min_df  \\\n",
-       "7                      0.05                       20   \n",
-       "10                      0.1                       20   \n",
-       "11                      0.1                      100   \n",
-       "8                      0.05                      100   \n",
-       "19                     0.05                       20   \n",
-       "23                      0.1                      100   \n",
-       "20                     0.05                      100   \n",
-       "22                      0.1                       20   \n",
-       "6                      0.05                        5   \n",
-       "5                       0.1                      100   \n",
-       "2                      0.05                      100   \n",
-       "9                       0.1                        5   \n",
-       "1                      0.05                       20   \n",
-       "4                       0.1                       20   \n",
-       "14                     0.05                      100   \n",
-       "17                      0.1                      100   \n",
-       "18                     0.05                        5   \n",
-       "13                     0.05                       20   \n",
-       "21                      0.1                        5   \n",
-       "16                      0.1                       20   \n",
-       "0                      0.05                        5   \n",
-       "3                       0.1                        5   \n",
-       "12                     0.05                        5   \n",
-       "15                      0.1                        5   \n",
-       "\n",
-       "                                               params  split0_test_score  \\\n",
-       "7   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.517618   \n",
-       "10  {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.513254   \n",
-       "11  {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.508306   \n",
-       "8   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.504070   \n",
-       "19  {'classifier__alpha': 1, 'classifier__fit_prio...           0.492205   \n",
-       "23  {'classifier__alpha': 1, 'classifier__fit_prio...           0.501203   \n",
-       "20  {'classifier__alpha': 1, 'classifier__fit_prio...           0.498869   \n",
-       "22  {'classifier__alpha': 1, 'classifier__fit_prio...           0.479585   \n",
-       "6   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.472578   \n",
-       "5   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.474821   \n",
-       "2   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.476218   \n",
-       "9   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.458994   \n",
-       "1   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.461036   \n",
-       "4   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.454689   \n",
-       "14  {'classifier__alpha': 1, 'classifier__fit_prio...           0.444039   \n",
-       "17  {'classifier__alpha': 1, 'classifier__fit_prio...           0.440663   \n",
-       "18  {'classifier__alpha': 1, 'classifier__fit_prio...           0.418284   \n",
-       "13  {'classifier__alpha': 1, 'classifier__fit_prio...           0.400471   \n",
-       "21  {'classifier__alpha': 1, 'classifier__fit_prio...           0.393559   \n",
-       "16  {'classifier__alpha': 1, 'classifier__fit_prio...           0.387535   \n",
-       "0   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.379471   \n",
-       "3   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.368234   \n",
-       "12  {'classifier__alpha': 1, 'classifier__fit_prio...           0.278087   \n",
-       "15  {'classifier__alpha': 1, 'classifier__fit_prio...           0.259347   \n",
-       "\n",
-       "    split1_test_score  split2_test_score  mean_test_score  std_test_score  \\\n",
-       "7            0.518688           0.513803         0.516703        0.002096   \n",
-       "10           0.517940           0.509116         0.513437        0.003605   \n",
-       "11           0.502244           0.501839         0.504129        0.002958   \n",
-       "8            0.502116           0.500603         0.502263        0.001419   \n",
-       "19           0.504466           0.495489         0.497387        0.005182   \n",
-       "23           0.493465           0.492361         0.495676        0.003934   \n",
-       "20           0.494357           0.492431         0.495219        0.002698   \n",
-       "22           0.496964           0.485209         0.487253        0.007241   \n",
-       "6            0.486362           0.481865         0.480268        0.005739   \n",
-       "5            0.474690           0.478096         0.475869        0.001576   \n",
-       "2            0.471377           0.478548         0.475381        0.002987   \n",
-       "9            0.475008           0.469998         0.468000        0.006689   \n",
-       "1            0.466296           0.467280         0.464871        0.002741   \n",
-       "4            0.463340           0.457743         0.458591        0.003582   \n",
-       "14           0.449822           0.450524         0.448128        0.002906   \n",
-       "17           0.448419           0.443441         0.444174        0.003209   \n",
-       "18           0.428063           0.428696         0.425014        0.004766   \n",
-       "13           0.406275           0.406521         0.404422        0.002796   \n",
-       "21           0.402869           0.406431         0.400953        0.005427   \n",
-       "16           0.392661           0.393992         0.391396        0.002784   \n",
-       "0            0.381348           0.386078         0.382299        0.002780   \n",
-       "3            0.367599           0.372849         0.369561        0.002339   \n",
-       "12           0.278157           0.278143         0.278129        0.000030   \n",
-       "15           0.262720           0.258716         0.260261        0.001758   \n",
-       "\n",
-       "    rank_test_score  \n",
-       "7                 1  \n",
-       "10                2  \n",
-       "11                3  \n",
-       "8                 4  \n",
-       "19                5  \n",
-       "23                6  \n",
-       "20                7  \n",
-       "22                8  \n",
-       "6                 9  \n",
-       "5                10  \n",
-       "2                11  \n",
-       "9                12  \n",
-       "1                13  \n",
-       "4                14  \n",
-       "14               15  \n",
-       "17               16  \n",
-       "18               17  \n",
-       "13               18  \n",
-       "21               19  \n",
-       "16               20  \n",
-       "0                21  \n",
-       "3                22  \n",
-       "12               23  \n",
-       "15               24  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'pd' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m/data/projects/great-ai/docs/examples/simple/train.ipynb Cell 6\u001b[0m in \u001b[0;36m<cell line: 18>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=2'>3</a>\u001b[0m optimisation_pipeline \u001b[39m=\u001b[39m GridSearchCV(\n\u001b[1;32m      <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=3'>4</a>\u001b[0m     create_pipeline(),\n\u001b[1;32m      <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=4'>5</a>\u001b[0m     {\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=13'>14</a>\u001b[0m     verbose\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=14'>15</a>\u001b[0m )\n\u001b[1;32m     <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=15'>16</a>\u001b[0m optimisation_pipeline\u001b[39m.\u001b[39mfit(X, y)\n\u001b[0;32m---> <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=17'>18</a>\u001b[0m results \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame(optimisation_pipeline\u001b[39m.\u001b[39mcv_results_)\n\u001b[1;32m     <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=18'>19</a>\u001b[0m results\u001b[39m.\u001b[39msort_values(\u001b[39m\"\u001b[39m\u001b[39mrank_test_score\u001b[39m\u001b[39m\"\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
+     ]
     }
    ],
    "source": [
@@ -783,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -825,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {