diff --git a/out.html b/out.html
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+spambase
+Loading spambase (Spambase Data Set)
+#instances=4601, type=<class 'numpy.ndarray'>, #features=57, #classes=[0 1], prevs=[0.606, 0.394]
+<table border="1" class="dataframe">
+  <thead>
+    <tr>
+      <th></th>
+      <th colspan="2" halign="left">base</th>
+      <th colspan="4" halign="left">true</th>
+      <th colspan="4" halign="left">estim</th>
+      <th colspan="7" halign="left">errors</th>
+    </tr>
+    <tr>
+      <th></th>
+      <th>0</th>
+      <th>1</th>
+      <th>T0</th>
+      <th>F1</th>
+      <th>F0</th>
+      <th>T1</th>
+      <th>T0</th>
+      <th>F1</th>
+      <th>F0</th>
+      <th>T1</th>
+      <th>mae</th>
+      <th>rae</th>
+      <th>mrae</th>
+      <th>kld</th>
+      <th>nkld</th>
+      <th>f1e_true</th>
+      <th>f1e_estim</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0.0000</td>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.0000</td>
+      <td>0.0038</td>
+      <td>0.0000</td>
+      <td>0.9962</td>
+      <td>0.0500</td>
+      <td>0.4571</td>
+      <td>0.4571</td>
+      <td>0.2254</td>
+      <td>0.1122</td>
+      <td>NaN</td>
+      <td>1.0000</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0.0000</td>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.0800</td>
+      <td>0.9200</td>
+      <td>0.0000</td>
+      <td>0.0008</td>
+      <td>0.0000</td>
+      <td>0.9992</td>
+      <td>0.0400</td>
+      <td>0.2981</td>
+      <td>0.2981</td>
+      <td>0.1641</td>
+      <td>0.0819</td>
+      <td>NaN</td>
+      <td>0.9907</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0.0000</td>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.1100</td>
+      <td>0.8900</td>
+      <td>0.0000</td>
+      <td>0.0012</td>
+      <td>0.0000</td>
+      <td>0.9988</td>
+      <td>0.0550</td>
+      <td>0.3289</td>
+      <td>0.3289</td>
+      <td>0.2568</td>
+      <td>0.1277</td>
+      <td>NaN</td>
+      <td>1.0000</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0.0000</td>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.0900</td>
+      <td>0.9100</td>
+      <td>0.0000</td>
+      <td>0.0073</td>
+      <td>0.0000</td>
+      <td>0.9927</td>
+      <td>0.0450</td>
+      <td>0.6231</td>
+      <td>0.6231</td>
+      <td>0.1960</td>
+      <td>0.0977</td>
+      <td>NaN</td>
+      <td>0.9999</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0.0000</td>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.0000</td>
+      <td>0.0178</td>
+      <td>0.0000</td>
+      <td>0.9822</td>
+      <td>0.0500</td>
+      <td>1.1522</td>
+      <td>1.1522</td>
+      <td>0.2334</td>
+      <td>0.1162</td>
+      <td>NaN</td>
+      <td>1.0000</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>0.0000</td>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.0600</td>
+      <td>0.9400</td>
+      <td>0.0000</td>
+      <td>0.0007</td>
+      <td>0.0000</td>
+      <td>0.9993</td>
+      <td>0.0300</td>
+      <td>0.2796</td>
+      <td>0.2796</td>
+      <td>0.1086</td>
+      <td>0.0542</td>
+      <td>NaN</td>
+      <td>1.0000</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>0.0000</td>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.1200</td>
+      <td>0.8800</td>
+      <td>0.0000</td>
+      <td>0.0008</td>
+      <td>0.0000</td>
+      <td>0.9992</td>
+      <td>0.0600</td>
+      <td>0.3150</td>
+      <td>0.3150</td>
+      <td>0.2898</td>
+      <td>0.1439</td>
+      <td>NaN</td>
+      <td>1.0000</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>0.0000</td>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.1100</td>
+      <td>0.8900</td>
+      <td>0.0000</td>
+      <td>0.0005</td>
+      <td>0.0000</td>
+      <td>0.9995</td>
+      <td>0.0550</td>
+      <td>0.2965</td>
+      <td>0.2965</td>
+      <td>0.2568</td>
+      <td>0.1277</td>
+      <td>NaN</td>
+      <td>1.0000</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>0.0000</td>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.0900</td>
+      <td>0.9100</td>
+      <td>0.0000</td>
+      <td>0.0009</td>
+      <td>0.0000</td>
+      <td>0.9991</td>
+      <td>0.0450</td>
+      <td>0.3055</td>
+      <td>0.3055</td>
+      <td>0.1939</td>
+      <td>0.0966</td>
+      <td>NaN</td>
+      <td>0.9761</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>0.0000</td>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.1400</td>
+      <td>0.8600</td>
+      <td>0.0000</td>
+      <td>0.0222</td>
+      <td>0.0000</td>
+      <td>0.9778</td>
+      <td>0.0700</td>
+      <td>1.3867</td>
+      <td>1.3867</td>
+      <td>0.3694</td>
+      <td>0.1826</td>
+      <td>NaN</td>
+      <td>1.0000</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.0900</td>
+      <td>0.0100</td>
+      <td>0.0800</td>
+      <td>0.8200</td>
+      <td>0.0085</td>
+      <td>0.2046</td>
+      <td>0.0000</td>
+      <td>0.7869</td>
+      <td>0.0973</td>
+      <td>3.7034</td>
+      <td>3.7034</td>
+      <td>0.4204</td>
+      <td>0.2072</td>
+      <td>0.3333</td>
+      <td>0.9233</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.0000</td>
+      <td>0.1100</td>
+      <td>0.7900</td>
+      <td>0.0288</td>
+      <td>0.0769</td>
+      <td>0.0000</td>
+      <td>0.8943</td>
+      <td>0.0906</td>
+      <td>4.2863</td>
+      <td>4.2863</td>
+      <td>0.3676</td>
+      <td>0.1817</td>
+      <td>0.3548</td>
+      <td>0.5715</td>
+    </tr>
+    <tr>
+      <th>12</th>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.0000</td>
+      <td>0.1000</td>
+      <td>0.8000</td>
+      <td>0.0649</td>
+      <td>0.0501</td>
+      <td>0.0000</td>
+      <td>0.8850</td>
+      <td>0.0675</td>
+      <td>2.8520</td>
+      <td>2.8520</td>
+      <td>0.2689</td>
+      <td>0.1337</td>
+      <td>0.3333</td>
+      <td>0.2786</td>
+    </tr>
+    <tr>
+      <th>13</th>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.0000</td>
+      <td>0.0600</td>
+      <td>0.8400</td>
+      <td>0.0182</td>
+      <td>0.1656</td>
+      <td>0.0000</td>
+      <td>0.8161</td>
+      <td>0.0828</td>
+      <td>8.7145</td>
+      <td>8.7145</td>
+      <td>0.3317</td>
+      <td>0.1643</td>
+      <td>0.2308</td>
+      <td>0.8196</td>
+    </tr>
+    <tr>
+      <th>14</th>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.0900</td>
+      <td>0.0100</td>
+      <td>0.0800</td>
+      <td>0.8200</td>
+      <td>0.0243</td>
+      <td>0.1076</td>
+      <td>0.0000</td>
+      <td>0.8681</td>
+      <td>0.0728</td>
+      <td>2.0492</td>
+      <td>2.0492</td>
+      <td>0.2755</td>
+      <td>0.1369</td>
+      <td>0.3333</td>
+      <td>0.6885</td>
+    </tr>
+    <tr>
+      <th>15</th>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.0000</td>
+      <td>0.0800</td>
+      <td>0.8200</td>
+      <td>0.0767</td>
+      <td>0.0219</td>
+      <td>0.0000</td>
+      <td>0.9015</td>
+      <td>0.0517</td>
+      <td>1.4088</td>
+      <td>1.4088</td>
+      <td>0.1811</td>
+      <td>0.0903</td>
+      <td>0.2857</td>
+      <td>0.1248</td>
+    </tr>
+    <tr>
+      <th>16</th>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.0800</td>
+      <td>0.0200</td>
+      <td>0.0700</td>
+      <td>0.8300</td>
+      <td>0.0176</td>
+      <td>0.0496</td>
+      <td>0.0001</td>
+      <td>0.9327</td>
+      <td>0.0662</td>
+      <td>0.7438</td>
+      <td>0.7438</td>
+      <td>0.1981</td>
+      <td>0.0987</td>
+      <td>0.3600</td>
+      <td>0.5856</td>
+    </tr>
+    <tr>
+      <th>17</th>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.0800</td>
+      <td>0.0200</td>
+      <td>0.1300</td>
+      <td>0.7700</td>
+      <td>0.0210</td>
+      <td>0.1940</td>
+      <td>0.0000</td>
+      <td>0.7850</td>
+      <td>0.0945</td>
+      <td>2.1586</td>
+      <td>2.1586</td>
+      <td>0.4788</td>
+      <td>0.2349</td>
+      <td>0.4839</td>
+      <td>0.8218</td>
+    </tr>
+    <tr>
+      <th>18</th>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.0800</td>
+      <td>0.0200</td>
+      <td>0.1200</td>
+      <td>0.7800</td>
+      <td>0.0178</td>
+      <td>0.1358</td>
+      <td>0.0000</td>
+      <td>0.8464</td>
+      <td>0.0911</td>
+      <td>1.6024</td>
+      <td>1.6024</td>
+      <td>0.4074</td>
+      <td>0.2009</td>
+      <td>0.4667</td>
+      <td>0.7928</td>
+    </tr>
+    <tr>
+      <th>19</th>
+      <td>0.1000</td>
+      <td>0.9000</td>
+      <td>0.0700</td>
+      <td>0.0300</td>
+      <td>0.1000</td>
+      <td>0.8000</td>
+      <td>0.0352</td>
+      <td>0.1377</td>
+      <td>0.0000</td>
+      <td>0.8271</td>
+      <td>0.0674</td>
+      <td>1.1316</td>
+      <td>1.1316</td>
+      <td>0.2906</td>
+      <td>0.1443</td>
+      <td>0.4815</td>
+      <td>0.6616</td>
+    </tr>
+    <tr>
+      <th>20</th>
+      <td>0.2000</td>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.0000</td>
+      <td>0.1100</td>
+      <td>0.6900</td>
+      <td>0.1176</td>
+      <td>0.2243</td>
+      <td>0.0000</td>
+      <td>0.6581</td>
+      <td>0.1122</td>
+      <td>11.5671</td>
+      <td>11.5671</td>
+      <td>0.4795</td>
+      <td>0.2353</td>
+      <td>0.2157</td>
+      <td>0.4881</td>
+    </tr>
+    <tr>
+      <th>21</th>
+      <td>0.2000</td>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.0000</td>
+      <td>0.0600</td>
+      <td>0.7400</td>
+      <td>0.1503</td>
+      <td>0.0440</td>
+      <td>0.0003</td>
+      <td>0.8054</td>
+      <td>0.0547</td>
+      <td>2.5108</td>
+      <td>2.5108</td>
+      <td>0.1455</td>
+      <td>0.0726</td>
+      <td>0.1304</td>
+      <td>0.1284</td>
+    </tr>
+    <tr>
+      <th>22</th>
+      <td>0.2000</td>
+      <td>0.8000</td>
+      <td>0.1800</td>
+      <td>0.0200</td>
+      <td>0.0700</td>
+      <td>0.7300</td>
+      <td>0.0650</td>
+      <td>0.1182</td>
+      <td>0.0000</td>
+      <td>0.8168</td>
+      <td>0.0925</td>
+      <td>1.4004</td>
+      <td>1.4004</td>
+      <td>0.2610</td>
+      <td>0.1297</td>
+      <td>0.2000</td>
+      <td>0.4762</td>
+    </tr>
+    <tr>
+      <th>23</th>
+      <td>0.2000</td>
+      <td>0.8000</td>
+      <td>0.1900</td>
+      <td>0.0100</td>
+      <td>0.1100</td>
+      <td>0.6900</td>
+      <td>0.1518</td>
+      <td>0.0209</td>
+      <td>0.0000</td>
+      <td>0.8273</td>
+      <td>0.0741</td>
+      <td>0.5186</td>
+      <td>0.5186</td>
+      <td>0.2696</td>
+      <td>0.1340</td>
+      <td>0.2400</td>
+      <td>0.0643</td>
+    </tr>
+    <tr>
+      <th>24</th>
+      <td>0.2000</td>
+      <td>0.8000</td>
+      <td>0.1900</td>
+      <td>0.0100</td>
+      <td>0.1100</td>
+      <td>0.6900</td>
+      <td>0.0676</td>
+      <td>0.3840</td>
+      <td>0.0000</td>
+      <td>0.5484</td>
+      <td>0.1870</td>
+      <td>6.6805</td>
+      <td>6.6805</td>
+      <td>0.6627</td>
+      <td>0.3197</td>
+      <td>0.2400</td>
+      <td>0.7395</td>
+    </tr>
+    <tr>
+      <th>25</th>
+      <td>0.2000</td>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.0000</td>
+      <td>0.0800</td>
+      <td>0.7200</td>
+      <td>0.1245</td>
+      <td>0.1006</td>
+      <td>0.0000</td>
+      <td>0.7749</td>
+      <td>0.0778</td>
+      <td>5.3763</td>
+      <td>5.3763</td>
+      <td>0.2668</td>
+      <td>0.1326</td>
+      <td>0.1667</td>
+      <td>0.2878</td>
+    </tr>
+    <tr>
+      <th>26</th>
+      <td>0.2000</td>
+      <td>0.8000</td>
+      <td>0.1900</td>
+      <td>0.0100</td>
+      <td>0.1000</td>
+      <td>0.7000</td>
+      <td>0.1407</td>
+      <td>0.0986</td>
+      <td>0.0000</td>
+      <td>0.7608</td>
+      <td>0.0747</td>
+      <td>1.7989</td>
+      <td>1.7989</td>
+      <td>0.2893</td>
+      <td>0.1436</td>
+      <td>0.2245</td>
+      <td>0.2594</td>
+    </tr>
+    <tr>
+      <th>27</th>
+      <td>0.2000</td>
+      <td>0.8000</td>
+      <td>0.1900</td>
+      <td>0.0100</td>
+      <td>0.0900</td>
+      <td>0.7100</td>
+      <td>0.0992</td>
+      <td>0.1236</td>
+      <td>0.0000</td>
+      <td>0.7772</td>
+      <td>0.0904</td>
+      <td>2.2699</td>
+      <td>2.2699</td>
+      <td>0.3055</td>
+      <td>0.1516</td>
+      <td>0.2083</td>
+      <td>0.3838</td>
+    </tr>
+    <tr>
+      <th>28</th>
+      <td>0.2000</td>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.0000</td>
+      <td>0.0700</td>
+      <td>0.7300</td>
+      <td>0.1562</td>
+      <td>0.0755</td>
+      <td>0.0000</td>
+      <td>0.7683</td>
+      <td>0.0569</td>
+      <td>4.0727</td>
+      <td>4.0727</td>
+      <td>0.2011</td>
+      <td>0.1002</td>
+      <td>0.1489</td>
+      <td>0.1945</td>
+    </tr>
+    <tr>
+      <th>29</th>
+      <td>0.2000</td>
+      <td>0.8000</td>
+      <td>0.1900</td>
+      <td>0.0100</td>
+      <td>0.1000</td>
+      <td>0.7000</td>
+      <td>0.1202</td>
+      <td>0.0460</td>
+      <td>0.0000</td>
+      <td>0.8338</td>
+      <td>0.0849</td>
+      <td>0.9743</td>
+      <td>0.9743</td>
+      <td>0.2652</td>
+      <td>0.1318</td>
+      <td>0.2245</td>
+      <td>0.1605</td>
+    </tr>
+    <tr>
+      <th>30</th>
+      <td>0.3000</td>
+      <td>0.7000</td>
+      <td>0.2900</td>
+      <td>0.0100</td>
+      <td>0.0800</td>
+      <td>0.6200</td>
+      <td>0.2028</td>
+      <td>0.1660</td>
+      <td>0.0000</td>
+      <td>0.6313</td>
+      <td>0.0836</td>
+      <td>2.9131</td>
+      <td>2.9131</td>
+      <td>0.2966</td>
+      <td>0.1472</td>
+      <td>0.1343</td>
+      <td>0.2904</td>
+    </tr>
+    <tr>
+      <th>31</th>
+      <td>0.3000</td>
+      <td>0.7000</td>
+      <td>0.2900</td>
+      <td>0.0100</td>
+      <td>0.0500</td>
+      <td>0.6500</td>
+      <td>0.2359</td>
+      <td>0.1008</td>
+      <td>0.0000</td>
+      <td>0.6634</td>
+      <td>0.0521</td>
+      <td>1.7908</td>
+      <td>1.7908</td>
+      <td>0.1491</td>
+      <td>0.0744</td>
+      <td>0.0938</td>
+      <td>0.1760</td>
+    </tr>
+    <tr>
+      <th>32</th>
+      <td>0.3000</td>
+      <td>0.7000</td>
+      <td>0.2700</td>
+      <td>0.0300</td>
+      <td>0.0700</td>
+      <td>0.6300</td>
+      <td>0.1881</td>
+      <td>0.2181</td>
+      <td>0.0000</td>
+      <td>0.5938</td>
+      <td>0.0940</td>
+      <td>1.6654</td>
+      <td>1.6654</td>
+      <td>0.2728</td>
+      <td>0.1355</td>
+      <td>0.1562</td>
+      <td>0.3670</td>
+    </tr>
+    <tr>
+      <th>33</th>
+      <td>0.3000</td>
+      <td>0.7000</td>
+      <td>0.2800</td>
+      <td>0.0200</td>
+      <td>0.0600</td>
+      <td>0.6400</td>
+      <td>0.2094</td>
+      <td>0.0879</td>
+      <td>0.0000</td>
+      <td>0.7028</td>
+      <td>0.0653</td>
+      <td>0.9956</td>
+      <td>0.9956</td>
+      <td>0.1552</td>
+      <td>0.0774</td>
+      <td>0.1250</td>
+      <td>0.1734</td>
+    </tr>
+    <tr>
+      <th>34</th>
+      <td>0.3000</td>
+      <td>0.7000</td>
+      <td>0.2800</td>
+      <td>0.0200</td>
+      <td>0.0300</td>
+      <td>0.6700</td>
+      <td>0.1789</td>
+      <td>0.0805</td>
+      <td>0.0000</td>
+      <td>0.7407</td>
+      <td>0.0656</td>
+      <td>0.9339</td>
+      <td>0.9339</td>
+      <td>0.0951</td>
+      <td>0.0475</td>
+      <td>0.0820</td>
+      <td>0.1836</td>
+    </tr>
+    <tr>
+      <th>35</th>
+      <td>0.3000</td>
+      <td>0.7000</td>
+      <td>0.2900</td>
+      <td>0.0100</td>
+      <td>0.1000</td>
+      <td>0.6000</td>
+      <td>0.1698</td>
+      <td>0.2419</td>
+      <td>0.0000</td>
+      <td>0.5884</td>
+      <td>0.1159</td>
+      <td>4.2090</td>
+      <td>4.2090</td>
+      <td>0.4438</td>
+      <td>0.2183</td>
+      <td>0.1594</td>
+      <td>0.4160</td>
+    </tr>
+    <tr>
+      <th>36</th>
+      <td>0.3000</td>
+      <td>0.7000</td>
+      <td>0.3000</td>
+      <td>0.0000</td>
+      <td>0.0800</td>
+      <td>0.6200</td>
+      <td>0.2506</td>
+      <td>0.0692</td>
+      <td>0.0000</td>
+      <td>0.6801</td>
+      <td>0.0647</td>
+      <td>3.7608</td>
+      <td>3.7608</td>
+      <td>0.2238</td>
+      <td>0.1114</td>
+      <td>0.1176</td>
+      <td>0.1213</td>
+    </tr>
+    <tr>
+      <th>37</th>
+      <td>0.3000</td>
+      <td>0.7000</td>
+      <td>0.2400</td>
+      <td>0.0600</td>
+      <td>0.0700</td>
+      <td>0.6300</td>
+      <td>0.0637</td>
+      <td>0.3139</td>
+      <td>0.0000</td>
+      <td>0.6225</td>
+      <td>0.1269</td>
+      <td>1.3927</td>
+      <td>1.3927</td>
+      <td>0.4190</td>
+      <td>0.2065</td>
+      <td>0.2131</td>
+      <td>0.7114</td>
+    </tr>
+    <tr>
+      <th>38</th>
+      <td>0.3000</td>
+      <td>0.7000</td>
+      <td>0.2700</td>
+      <td>0.0300</td>
+      <td>0.0600</td>
+      <td>0.6400</td>
+      <td>0.1499</td>
+      <td>0.2303</td>
+      <td>0.0000</td>
+      <td>0.6198</td>
+      <td>0.1001</td>
+      <td>1.7782</td>
+      <td>1.7782</td>
+      <td>0.2783</td>
+      <td>0.1383</td>
+      <td>0.1429</td>
+      <td>0.4343</td>
+    </tr>
+    <tr>
+      <th>39</th>
+      <td>0.3000</td>
+      <td>0.7000</td>
+      <td>0.2800</td>
+      <td>0.0200</td>
+      <td>0.0400</td>
+      <td>0.6600</td>
+      <td>0.1516</td>
+      <td>0.1818</td>
+      <td>0.0000</td>
+      <td>0.6666</td>
+      <td>0.0842</td>
+      <td>1.9554</td>
+      <td>1.9554</td>
+      <td>0.2127</td>
+      <td>0.1060</td>
+      <td>0.0968</td>
+      <td>0.3749</td>
+    </tr>
+    <tr>
+      <th>40</th>
+      <td>0.4000</td>
+      <td>0.6000</td>
+      <td>0.3900</td>
+      <td>0.0100</td>
+      <td>0.0700</td>
+      <td>0.5300</td>
+      <td>0.2261</td>
+      <td>0.3143</td>
+      <td>0.0000</td>
+      <td>0.4595</td>
+      <td>0.1522</td>
+      <td>5.4423</td>
+      <td>5.4423</td>
+      <td>0.4445</td>
+      <td>0.2187</td>
+      <td>0.0930</td>
+      <td>0.4100</td>
+    </tr>
+    <tr>
+      <th>41</th>
+      <td>0.4000</td>
+      <td>0.6000</td>
+      <td>0.3500</td>
+      <td>0.0500</td>
+      <td>0.0500</td>
+      <td>0.5500</td>
+      <td>0.2613</td>
+      <td>0.1893</td>
+      <td>0.0000</td>
+      <td>0.5494</td>
+      <td>0.0697</td>
+      <td>0.9233</td>
+      <td>0.9233</td>
+      <td>0.1652</td>
+      <td>0.0824</td>
+      <td>0.1250</td>
+      <td>0.2660</td>
+    </tr>
+    <tr>
+      <th>42</th>
+      <td>0.4000</td>
+      <td>0.6000</td>
+      <td>0.3800</td>
+      <td>0.0200</td>
+      <td>0.0800</td>
+      <td>0.5200</td>
+      <td>0.2133</td>
+      <td>0.2490</td>
+      <td>0.0000</td>
+      <td>0.5377</td>
+      <td>0.1234</td>
+      <td>2.6422</td>
+      <td>2.6422</td>
+      <td>0.3839</td>
+      <td>0.1896</td>
+      <td>0.1163</td>
+      <td>0.3686</td>
+    </tr>
+    <tr>
+      <th>43</th>
+      <td>0.4000</td>
+      <td>0.6000</td>
+      <td>0.3900</td>
+      <td>0.0100</td>
+      <td>0.0500</td>
+      <td>0.5500</td>
+      <td>0.2247</td>
+      <td>0.3492</td>
+      <td>0.0000</td>
+      <td>0.4261</td>
+      <td>0.1696</td>
+      <td>6.0405</td>
+      <td>6.0405</td>
+      <td>0.4388</td>
+      <td>0.2159</td>
+      <td>0.0714</td>
+      <td>0.4373</td>
+    </tr>
+    <tr>
+      <th>44</th>
+      <td>0.4000</td>
+      <td>0.6000</td>
+      <td>0.3800</td>
+      <td>0.0200</td>
+      <td>0.0900</td>
+      <td>0.5100</td>
+      <td>0.2634</td>
+      <td>0.2214</td>
+      <td>0.0000</td>
+      <td>0.5152</td>
+      <td>0.1033</td>
+      <td>2.3290</td>
+      <td>2.3290</td>
+      <td>0.3583</td>
+      <td>0.1773</td>
+      <td>0.1264</td>
+      <td>0.2959</td>
+    </tr>
+    <tr>
+      <th>45</th>
+      <td>0.4000</td>
+      <td>0.6000</td>
+      <td>0.3800</td>
+      <td>0.0200</td>
+      <td>0.0600</td>
+      <td>0.5400</td>
+      <td>0.2125</td>
+      <td>0.2861</td>
+      <td>0.0000</td>
+      <td>0.5014</td>
+      <td>0.1331</td>
+      <td>3.0184</td>
+      <td>3.0184</td>
+      <td>0.3653</td>
+      <td>0.1806</td>
+      <td>0.0952</td>
+      <td>0.4024</td>
+    </tr>
+    <tr>
+      <th>46</th>
+      <td>0.4000</td>
+      <td>0.6000</td>
+      <td>0.3900</td>
+      <td>0.0100</td>
+      <td>0.0500</td>
+      <td>0.5500</td>
+      <td>0.3225</td>
+      <td>0.1608</td>
+      <td>0.0000</td>
+      <td>0.5166</td>
+      <td>0.0754</td>
+      <td>2.7990</td>
+      <td>2.7990</td>
+      <td>0.2042</td>
+      <td>0.1018</td>
+      <td>0.0714</td>
+      <td>0.1996</td>
+    </tr>
+    <tr>
+      <th>47</th>
+      <td>0.4000</td>
+      <td>0.6000</td>
+      <td>0.3600</td>
+      <td>0.0400</td>
+      <td>0.0500</td>
+      <td>0.5500</td>
+      <td>0.1931</td>
+      <td>0.3664</td>
+      <td>0.0000</td>
+      <td>0.4405</td>
+      <td>0.1632</td>
+      <td>2.2041</td>
+      <td>2.2041</td>
+      <td>0.3819</td>
+      <td>0.1887</td>
+      <td>0.1111</td>
+      <td>0.4868</td>
+    </tr>
+    <tr>
+      <th>48</th>
+      <td>0.4000</td>
+      <td>0.6000</td>
+      <td>0.3500</td>
+      <td>0.0500</td>
+      <td>0.0400</td>
+      <td>0.5600</td>
+      <td>0.2412</td>
+      <td>0.1834</td>
+      <td>0.0000</td>
+      <td>0.5754</td>
+      <td>0.0744</td>
+      <td>0.9119</td>
+      <td>0.9119</td>
+      <td>0.1458</td>
+      <td>0.0728</td>
+      <td>0.1139</td>
+      <td>0.2754</td>
+    </tr>
+    <tr>
+      <th>49</th>
+      <td>0.4000</td>
+      <td>0.6000</td>
+      <td>0.3800</td>
+      <td>0.0200</td>
+      <td>0.0800</td>
+      <td>0.5200</td>
+      <td>0.2901</td>
+      <td>0.1835</td>
+      <td>0.0000</td>
+      <td>0.5264</td>
+      <td>0.0849</td>
+      <td>1.9318</td>
+      <td>1.9318</td>
+      <td>0.2863</td>
+      <td>0.1422</td>
+      <td>0.1163</td>
+      <td>0.2403</td>
+    </tr>
+    <tr>
+      <th>50</th>
+      <td>0.5000</td>
+      <td>0.5000</td>
+      <td>0.4700</td>
+      <td>0.0300</td>
+      <td>0.0200</td>
+      <td>0.4800</td>
+      <td>0.2661</td>
+      <td>0.2548</td>
+      <td>0.0000</td>
+      <td>0.4791</td>
+      <td>0.1124</td>
+      <td>1.9135</td>
+      <td>1.9135</td>
+      <td>0.2373</td>
+      <td>0.1181</td>
+      <td>0.0505</td>
+      <td>0.3237</td>
+    </tr>
+    <tr>
+      <th>51</th>
+      <td>0.5000</td>
+      <td>0.5000</td>
+      <td>0.4600</td>
+      <td>0.0400</td>
+      <td>0.0700</td>
+      <td>0.4300</td>
+      <td>0.3066</td>
+      <td>0.2872</td>
+      <td>0.0000</td>
+      <td>0.4062</td>
+      <td>0.1236</td>
+      <td>1.7029</td>
+      <td>1.7029</td>
+      <td>0.3295</td>
+      <td>0.1633</td>
+      <td>0.1068</td>
+      <td>0.3190</td>
+    </tr>
+    <tr>
+      <th>52</th>
+      <td>0.5000</td>
+      <td>0.5000</td>
+      <td>0.4900</td>
+      <td>0.0100</td>
+      <td>0.0700</td>
+      <td>0.4300</td>
+      <td>0.4488</td>
+      <td>0.0499</td>
+      <td>0.0000</td>
+      <td>0.5013</td>
+      <td>0.0556</td>
+      <td>0.9601</td>
+      <td>0.9601</td>
+      <td>0.1606</td>
+      <td>0.0801</td>
+      <td>0.0755</td>
+      <td>0.0527</td>
+    </tr>
+    <tr>
+      <th>53</th>
+      <td>0.5000</td>
+      <td>0.5000</td>
+      <td>0.4700</td>
+      <td>0.0300</td>
+      <td>0.0100</td>
+      <td>0.4900</td>
+      <td>0.3117</td>
+      <td>0.2813</td>
+      <td>0.0000</td>
+      <td>0.4070</td>
+      <td>0.1257</td>
+      <td>2.0871</td>
+      <td>2.0871</td>
+      <td>0.2263</td>
+      <td>0.1127</td>
+      <td>0.0408</td>
+      <td>0.3109</td>
+    </tr>
+    <tr>
+      <th>54</th>
+      <td>0.5000</td>
+      <td>0.5000</td>
+      <td>0.4700</td>
+      <td>0.0300</td>
+      <td>0.0500</td>
+      <td>0.4500</td>
+      <td>0.3857</td>
+      <td>0.1325</td>
+      <td>0.0000</td>
+      <td>0.4818</td>
+      <td>0.0672</td>
+      <td>1.0215</td>
+      <td>1.0215</td>
+      <td>0.1461</td>
+      <td>0.0729</td>
+      <td>0.0784</td>
+      <td>0.1466</td>
+    </tr>
+    <tr>
+      <th>55</th>
+      <td>0.5000</td>
+      <td>0.5000</td>
+      <td>0.4500</td>
+      <td>0.0500</td>
+      <td>0.0900</td>
+      <td>0.4100</td>
+      <td>0.3611</td>
+      <td>0.2164</td>
+      <td>0.0000</td>
+      <td>0.4224</td>
+      <td>0.0894</td>
+      <td>1.0498</td>
+      <td>1.0498</td>
+      <td>0.2897</td>
+      <td>0.1438</td>
+      <td>0.1346</td>
+      <td>0.2306</td>
+    </tr>
+    <tr>
+      <th>56</th>
+      <td>0.5000</td>
+      <td>0.5000</td>
+      <td>0.4300</td>
+      <td>0.0700</td>
+      <td>0.0700</td>
+      <td>0.4300</td>
+      <td>0.2665</td>
+      <td>0.3518</td>
+      <td>0.0000</td>
+      <td>0.3817</td>
+      <td>0.1409</td>
+      <td>1.2942</td>
+      <td>1.2942</td>
+      <td>0.3423</td>
+      <td>0.1695</td>
+      <td>0.1400</td>
+      <td>0.3975</td>
+    </tr>
+    <tr>
+      <th>57</th>
+      <td>0.5000</td>
+      <td>0.5000</td>
+      <td>0.4600</td>
+      <td>0.0400</td>
+      <td>0.0300</td>
+      <td>0.4700</td>
+      <td>0.2969</td>
+      <td>0.2003</td>
+      <td>0.0000</td>
+      <td>0.5028</td>
+      <td>0.0966</td>
+      <td>1.2099</td>
+      <td>1.2099</td>
+      <td>0.1690</td>
+      <td>0.0843</td>
+      <td>0.0707</td>
+      <td>0.2523</td>
+    </tr>
+    <tr>
+      <th>58</th>
+      <td>0.5000</td>
+      <td>0.5000</td>
+      <td>0.4900</td>
+      <td>0.0100</td>
+      <td>0.0200</td>
+      <td>0.4800</td>
+      <td>0.4031</td>
+      <td>0.0720</td>
+      <td>0.0000</td>
+      <td>0.5249</td>
+      <td>0.0534</td>
+      <td>1.3003</td>
+      <td>1.3003</td>
+      <td>0.0683</td>
+      <td>0.0341</td>
+      <td>0.0297</td>
+      <td>0.0820</td>
+    </tr>
+    <tr>
+      <th>59</th>
+      <td>0.5000</td>
+      <td>0.5000</td>
+      <td>0.4900</td>
+      <td>0.0100</td>
+      <td>0.0600</td>
+      <td>0.4400</td>
+      <td>0.3352</td>
+      <td>0.3022</td>
+      <td>0.0000</td>
+      <td>0.3626</td>
+      <td>0.1461</td>
+      <td>5.2225</td>
+      <td>5.2225</td>
+      <td>0.3921</td>
+      <td>0.1936</td>
+      <td>0.0667</td>
+      <td>0.3107</td>
+    </tr>
+    <tr>
+      <th>60</th>
+      <td>0.6000</td>
+      <td>0.4000</td>
+      <td>0.5500</td>
+      <td>0.0500</td>
+      <td>0.0200</td>
+      <td>0.3800</td>
+      <td>0.3723</td>
+      <td>0.2617</td>
+      <td>0.0000</td>
+      <td>0.3660</td>
+      <td>0.1059</td>
+      <td>1.2515</td>
+      <td>1.2515</td>
+      <td>0.1819</td>
+      <td>0.0907</td>
+      <td>0.0598</td>
+      <td>0.2601</td>
+    </tr>
+    <tr>
+      <th>61</th>
+      <td>0.6000</td>
+      <td>0.4000</td>
+      <td>0.5500</td>
+      <td>0.0500</td>
+      <td>0.0600</td>
+      <td>0.3400</td>
+      <td>0.3944</td>
+      <td>0.3478</td>
+      <td>0.0000</td>
+      <td>0.2577</td>
+      <td>0.1489</td>
+      <td>1.7143</td>
+      <td>1.7143</td>
+      <td>0.3410</td>
+      <td>0.1689</td>
+      <td>0.0909</td>
+      <td>0.3060</td>
+    </tr>
+    <tr>
+      <th>62</th>
+      <td>0.6000</td>
+      <td>0.4000</td>
+      <td>0.5800</td>
+      <td>0.0200</td>
+      <td>0.0600</td>
+      <td>0.3400</td>
+      <td>0.4883</td>
+      <td>0.1869</td>
+      <td>0.0000</td>
+      <td>0.3248</td>
+      <td>0.0835</td>
+      <td>1.9503</td>
+      <td>1.9503</td>
+      <td>0.2311</td>
+      <td>0.1150</td>
+      <td>0.0645</td>
+      <td>0.1607</td>
+    </tr>
+    <tr>
+      <th>63</th>
+      <td>0.6000</td>
+      <td>0.4000</td>
+      <td>0.5900</td>
+      <td>0.0100</td>
+      <td>0.0300</td>
+      <td>0.3700</td>
+      <td>0.4691</td>
+      <td>0.3098</td>
+      <td>0.0000</td>
+      <td>0.2211</td>
+      <td>0.1499</td>
+      <td>5.3611</td>
+      <td>5.3611</td>
+      <td>0.3474</td>
+      <td>0.1719</td>
+      <td>0.0328</td>
+      <td>0.2482</td>
+    </tr>
+    <tr>
+      <th>64</th>
+      <td>0.6000</td>
+      <td>0.4000</td>
+      <td>0.5700</td>
+      <td>0.0300</td>
+      <td>0.0600</td>
+      <td>0.3400</td>
+      <td>0.4036</td>
+      <td>0.3264</td>
+      <td>0.0000</td>
+      <td>0.2700</td>
+      <td>0.1482</td>
+      <td>2.4709</td>
+      <td>2.4709</td>
+      <td>0.3627</td>
+      <td>0.1794</td>
+      <td>0.0732</td>
+      <td>0.2879</td>
+    </tr>
+    <tr>
+      <th>65</th>
+      <td>0.6000</td>
+      <td>0.4000</td>
+      <td>0.5700</td>
+      <td>0.0300</td>
+      <td>0.0100</td>
+      <td>0.3900</td>
+      <td>0.3251</td>
+      <td>0.3488</td>
+      <td>0.0000</td>
+      <td>0.3262</td>
+      <td>0.1594</td>
+      <td>2.5904</td>
+      <td>2.5904</td>
+      <td>0.3243</td>
+      <td>0.1607</td>
+      <td>0.0339</td>
+      <td>0.3491</td>
+    </tr>
+    <tr>
+      <th>66</th>
+      <td>0.6000</td>
+      <td>0.4000</td>
+      <td>0.5300</td>
+      <td>0.0700</td>
+      <td>0.0500</td>
+      <td>0.3500</td>
+      <td>0.2977</td>
+      <td>0.4788</td>
+      <td>0.0000</td>
+      <td>0.2235</td>
+      <td>0.2044</td>
+      <td>1.7875</td>
+      <td>1.7875</td>
+      <td>0.4531</td>
+      <td>0.2228</td>
+      <td>0.1017</td>
+      <td>0.4457</td>
+    </tr>
+    <tr>
+      <th>67</th>
+      <td>0.6000</td>
+      <td>0.4000</td>
+      <td>0.5600</td>
+      <td>0.0400</td>
+      <td>0.0600</td>
+      <td>0.3400</td>
+      <td>0.4491</td>
+      <td>0.2367</td>
+      <td>0.0000</td>
+      <td>0.3142</td>
+      <td>0.0983</td>
+      <td>1.3912</td>
+      <td>1.3912</td>
+      <td>0.2413</td>
+      <td>0.1201</td>
+      <td>0.0820</td>
+      <td>0.2086</td>
+    </tr>
+    <tr>
+      <th>68</th>
+      <td>0.6000</td>
+      <td>0.4000</td>
+      <td>0.5600</td>
+      <td>0.0400</td>
+      <td>0.0400</td>
+      <td>0.3600</td>
+      <td>0.4048</td>
+      <td>0.3060</td>
+      <td>0.0000</td>
+      <td>0.2892</td>
+      <td>0.1330</td>
+      <td>1.8169</td>
+      <td>1.8169</td>
+      <td>0.2720</td>
+      <td>0.1352</td>
+      <td>0.0667</td>
+      <td>0.2743</td>
+    </tr>
+    <tr>
+      <th>69</th>
+      <td>0.6000</td>
+      <td>0.4000</td>
+      <td>0.5900</td>
+      <td>0.0100</td>
+      <td>0.0300</td>
+      <td>0.3700</td>
+      <td>0.4462</td>
+      <td>0.2388</td>
+      <td>0.0000</td>
+      <td>0.3150</td>
+      <td>0.1144</td>
+      <td>4.1244</td>
+      <td>4.1244</td>
+      <td>0.2503</td>
+      <td>0.1245</td>
+      <td>0.0328</td>
+      <td>0.2111</td>
+    </tr>
+    <tr>
+      <th>70</th>
+      <td>0.7000</td>
+      <td>0.3000</td>
+      <td>0.6900</td>
+      <td>0.0100</td>
+      <td>0.0200</td>
+      <td>0.2800</td>
+      <td>0.6374</td>
+      <td>0.0534</td>
+      <td>0.0000</td>
+      <td>0.3093</td>
+      <td>0.0363</td>
+      <td>0.9679</td>
+      <td>0.9679</td>
+      <td>0.0468</td>
+      <td>0.0234</td>
+      <td>0.0213</td>
+      <td>0.0402</td>
+    </tr>
+    <tr>
+      <th>71</th>
+      <td>0.7000</td>
+      <td>0.3000</td>
+      <td>0.6600</td>
+      <td>0.0400</td>
+      <td>0.0100</td>
+      <td>0.2900</td>
+      <td>0.5646</td>
+      <td>0.2108</td>
+      <td>0.0000</td>
+      <td>0.2246</td>
+      <td>0.0854</td>
+      <td>1.2071</td>
+      <td>1.2071</td>
+      <td>0.1229</td>
+      <td>0.0614</td>
+      <td>0.0365</td>
+      <td>0.1573</td>
+    </tr>
+    <tr>
+      <th>72</th>
+      <td>0.7000</td>
+      <td>0.3000</td>
+      <td>0.6400</td>
+      <td>0.0600</td>
+      <td>0.0300</td>
+      <td>0.2700</td>
+      <td>0.5626</td>
+      <td>0.1337</td>
+      <td>0.0000</td>
+      <td>0.3037</td>
+      <td>0.0537</td>
+      <td>0.5584</td>
+      <td>0.5584</td>
+      <td>0.0695</td>
+      <td>0.0347</td>
+      <td>0.0657</td>
+      <td>0.1062</td>
+    </tr>
+    <tr>
+      <th>73</th>
+      <td>0.7000</td>
+      <td>0.3000</td>
+      <td>0.6400</td>
+      <td>0.0600</td>
+      <td>0.0100</td>
+      <td>0.2900</td>
+      <td>0.4890</td>
+      <td>0.3280</td>
+      <td>0.0000</td>
+      <td>0.1831</td>
+      <td>0.1340</td>
+      <td>1.3465</td>
+      <td>1.3465</td>
+      <td>0.2152</td>
+      <td>0.1072</td>
+      <td>0.0519</td>
+      <td>0.2512</td>
+    </tr>
+    <tr>
+      <th>74</th>
+      <td>0.7000</td>
+      <td>0.3000</td>
+      <td>0.6300</td>
+      <td>0.0700</td>
+      <td>0.0200</td>
+      <td>0.2800</td>
+      <td>0.4717</td>
+      <td>0.2827</td>
+      <td>0.0000</td>
+      <td>0.2456</td>
+      <td>0.1063</td>
+      <td>1.0015</td>
+      <td>1.0015</td>
+      <td>0.1581</td>
+      <td>0.0789</td>
+      <td>0.0667</td>
+      <td>0.2305</td>
+    </tr>
+    <tr>
+      <th>75</th>
+      <td>0.7000</td>
+      <td>0.3000</td>
+      <td>0.6800</td>
+      <td>0.0200</td>
+      <td>0.0300</td>
+      <td>0.2700</td>
+      <td>0.5914</td>
+      <td>0.2123</td>
+      <td>0.0000</td>
+      <td>0.1963</td>
+      <td>0.0961</td>
+      <td>2.2366</td>
+      <td>2.2366</td>
+      <td>0.1947</td>
+      <td>0.0970</td>
+      <td>0.0355</td>
+      <td>0.1522</td>
+    </tr>
+    <tr>
+      <th>76</th>
+      <td>0.7000</td>
+      <td>0.3000</td>
+      <td>0.6600</td>
+      <td>0.0400</td>
+      <td>0.0300</td>
+      <td>0.2700</td>
+      <td>0.6164</td>
+      <td>0.1300</td>
+      <td>0.0000</td>
+      <td>0.2535</td>
+      <td>0.0450</td>
+      <td>0.7457</td>
+      <td>0.7457</td>
+      <td>0.0807</td>
+      <td>0.0403</td>
+      <td>0.0504</td>
+      <td>0.0954</td>
+    </tr>
+    <tr>
+      <th>77</th>
+      <td>0.7000</td>
+      <td>0.3000</td>
+      <td>0.6600</td>
+      <td>0.0400</td>
+      <td>0.0100</td>
+      <td>0.2900</td>
+      <td>0.5485</td>
+      <td>0.1940</td>
+      <td>0.0000</td>
+      <td>0.2575</td>
+      <td>0.0770</td>
+      <td>1.0917</td>
+      <td>1.0917</td>
+      <td>0.1061</td>
+      <td>0.0530</td>
+      <td>0.0365</td>
+      <td>0.1503</td>
+    </tr>
+    <tr>
+      <th>78</th>
+      <td>0.7000</td>
+      <td>0.3000</td>
+      <td>0.6600</td>
+      <td>0.0400</td>
+      <td>0.0500</td>
+      <td>0.2500</td>
+      <td>0.5583</td>
+      <td>0.2694</td>
+      <td>0.0000</td>
+      <td>0.1723</td>
+      <td>0.1147</td>
+      <td>1.6161</td>
+      <td>1.6161</td>
+      <td>0.2536</td>
+      <td>0.1261</td>
+      <td>0.0638</td>
+      <td>0.1944</td>
+    </tr>
+    <tr>
+      <th>79</th>
+      <td>0.7000</td>
+      <td>0.3000</td>
+      <td>0.6800</td>
+      <td>0.0200</td>
+      <td>0.0300</td>
+      <td>0.2700</td>
+      <td>0.4831</td>
+      <td>0.2938</td>
+      <td>0.0000</td>
+      <td>0.2231</td>
+      <td>0.1369</td>
+      <td>3.0667</td>
+      <td>3.0667</td>
+      <td>0.2896</td>
+      <td>0.1438</td>
+      <td>0.0355</td>
+      <td>0.2332</td>
+    </tr>
+    <tr>
+      <th>80</th>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.7300</td>
+      <td>0.0700</td>
+      <td>0.0300</td>
+      <td>0.1700</td>
+      <td>0.5847</td>
+      <td>0.3097</td>
+      <td>0.0000</td>
+      <td>0.1056</td>
+      <td>0.1199</td>
+      <td>1.1547</td>
+      <td>1.1547</td>
+      <td>0.2027</td>
+      <td>0.1010</td>
+      <td>0.0641</td>
+      <td>0.2094</td>
+    </tr>
+    <tr>
+      <th>81</th>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.7300</td>
+      <td>0.0700</td>
+      <td>0.0300</td>
+      <td>0.1700</td>
+      <td>0.5118</td>
+      <td>0.3332</td>
+      <td>0.0000</td>
+      <td>0.1550</td>
+      <td>0.1316</td>
+      <td>1.1873</td>
+      <td>1.1873</td>
+      <td>0.2297</td>
+      <td>0.1144</td>
+      <td>0.0641</td>
+      <td>0.2456</td>
+    </tr>
+    <tr>
+      <th>82</th>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.7100</td>
+      <td>0.0900</td>
+      <td>0.0000</td>
+      <td>0.2000</td>
+      <td>0.5372</td>
+      <td>0.2500</td>
+      <td>0.0000</td>
+      <td>0.2127</td>
+      <td>0.0864</td>
+      <td>0.4987</td>
+      <td>0.4987</td>
+      <td>0.0916</td>
+      <td>0.0457</td>
+      <td>0.0596</td>
+      <td>0.1888</td>
+    </tr>
+    <tr>
+      <th>83</th>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.7500</td>
+      <td>0.0500</td>
+      <td>0.0700</td>
+      <td>0.1300</td>
+      <td>0.7568</td>
+      <td>0.1519</td>
+      <td>0.0000</td>
+      <td>0.0913</td>
+      <td>0.0544</td>
+      <td>0.7703</td>
+      <td>0.7703</td>
+      <td>0.1843</td>
+      <td>0.0919</td>
+      <td>0.0741</td>
+      <td>0.0912</td>
+    </tr>
+    <tr>
+      <th>84</th>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.7600</td>
+      <td>0.0400</td>
+      <td>0.0200</td>
+      <td>0.1800</td>
+      <td>0.7005</td>
+      <td>0.2142</td>
+      <td>0.0000</td>
+      <td>0.0853</td>
+      <td>0.0871</td>
+      <td>1.3152</td>
+      <td>1.3152</td>
+      <td>0.1637</td>
+      <td>0.0816</td>
+      <td>0.0380</td>
+      <td>0.1326</td>
+    </tr>
+    <tr>
+      <th>85</th>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.7500</td>
+      <td>0.0500</td>
+      <td>0.0100</td>
+      <td>0.1900</td>
+      <td>0.6569</td>
+      <td>0.1493</td>
+      <td>0.0000</td>
+      <td>0.1938</td>
+      <td>0.0516</td>
+      <td>0.6539</td>
+      <td>0.6539</td>
+      <td>0.0554</td>
+      <td>0.0277</td>
+      <td>0.0385</td>
+      <td>0.1021</td>
+    </tr>
+    <tr>
+      <th>86</th>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.7600</td>
+      <td>0.0400</td>
+      <td>0.0100</td>
+      <td>0.1900</td>
+      <td>0.6134</td>
+      <td>0.2222</td>
+      <td>0.0000</td>
+      <td>0.1644</td>
+      <td>0.0911</td>
+      <td>1.2596</td>
+      <td>1.2596</td>
+      <td>0.1338</td>
+      <td>0.0668</td>
+      <td>0.0318</td>
+      <td>0.1534</td>
+    </tr>
+    <tr>
+      <th>87</th>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.7500</td>
+      <td>0.0500</td>
+      <td>0.0100</td>
+      <td>0.1900</td>
+      <td>0.6629</td>
+      <td>0.2320</td>
+      <td>0.0000</td>
+      <td>0.1051</td>
+      <td>0.0910</td>
+      <td>1.1313</td>
+      <td>1.1313</td>
+      <td>0.1401</td>
+      <td>0.0699</td>
+      <td>0.0385</td>
+      <td>0.1489</td>
+    </tr>
+    <tr>
+      <th>88</th>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.7400</td>
+      <td>0.0600</td>
+      <td>0.0000</td>
+      <td>0.2000</td>
+      <td>0.5857</td>
+      <td>0.3081</td>
+      <td>0.0000</td>
+      <td>0.1062</td>
+      <td>0.1240</td>
+      <td>1.1204</td>
+      <td>1.1204</td>
+      <td>0.1961</td>
+      <td>0.0977</td>
+      <td>0.0390</td>
+      <td>0.2082</td>
+    </tr>
+    <tr>
+      <th>89</th>
+      <td>0.8000</td>
+      <td>0.2000</td>
+      <td>0.7400</td>
+      <td>0.0600</td>
+      <td>0.0200</td>
+      <td>0.1800</td>
+      <td>0.4581</td>
+      <td>0.4695</td>
+      <td>0.0000</td>
+      <td>0.0724</td>
+      <td>0.2048</td>
+      <td>2.0152</td>
+      <td>2.0152</td>
+      <td>0.4265</td>
+      <td>0.2101</td>
+      <td>0.0513</td>
+      <td>0.3389</td>
+    </tr>
+    <tr>
+      <th>90</th>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.8600</td>
+      <td>0.0400</td>
+      <td>0.0200</td>
+      <td>0.0800</td>
+      <td>0.8681</td>
+      <td>0.1034</td>
+      <td>0.0000</td>
+      <td>0.0284</td>
+      <td>0.0358</td>
+      <td>0.7063</td>
+      <td>0.7063</td>
+      <td>0.0718</td>
+      <td>0.0359</td>
+      <td>0.0337</td>
+      <td>0.0562</td>
+    </tr>
+    <tr>
+      <th>91</th>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.8200</td>
+      <td>0.0800</td>
+      <td>0.0200</td>
+      <td>0.0800</td>
+      <td>0.6954</td>
+      <td>0.2312</td>
+      <td>0.0002</td>
+      <td>0.0732</td>
+      <td>0.0756</td>
+      <td>0.7002</td>
+      <td>0.7002</td>
+      <td>0.0945</td>
+      <td>0.0472</td>
+      <td>0.0575</td>
+      <td>0.1426</td>
+    </tr>
+    <tr>
+      <th>92</th>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.8500</td>
+      <td>0.0500</td>
+      <td>0.0100</td>
+      <td>0.0900</td>
+      <td>0.7136</td>
+      <td>0.2209</td>
+      <td>0.0001</td>
+      <td>0.0654</td>
+      <td>0.0854</td>
+      <td>1.0458</td>
+      <td>1.0458</td>
+      <td>0.1155</td>
+      <td>0.0577</td>
+      <td>0.0341</td>
+      <td>0.1341</td>
+    </tr>
+    <tr>
+      <th>93</th>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.8300</td>
+      <td>0.0700</td>
+      <td>0.0100</td>
+      <td>0.0900</td>
+      <td>0.6537</td>
+      <td>0.2912</td>
+      <td>0.0000</td>
+      <td>0.0552</td>
+      <td>0.1106</td>
+      <td>1.0483</td>
+      <td>1.0483</td>
+      <td>0.1549</td>
+      <td>0.0773</td>
+      <td>0.0460</td>
+      <td>0.1822</td>
+    </tr>
+    <tr>
+      <th>94</th>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.8300</td>
+      <td>0.0700</td>
+      <td>0.0000</td>
+      <td>0.1000</td>
+      <td>0.8285</td>
+      <td>0.0810</td>
+      <td>0.0000</td>
+      <td>0.0905</td>
+      <td>0.0055</td>
+      <td>0.0602</td>
+      <td>0.0602</td>
+      <td>0.0012</td>
+      <td>0.0006</td>
+      <td>0.0405</td>
+      <td>0.0466</td>
+    </tr>
+    <tr>
+      <th>95</th>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.8500</td>
+      <td>0.0500</td>
+      <td>0.0000</td>
+      <td>0.1000</td>
+      <td>0.7245</td>
+      <td>0.2244</td>
+      <td>0.0000</td>
+      <td>0.0511</td>
+      <td>0.0872</td>
+      <td>0.9459</td>
+      <td>0.9459</td>
+      <td>0.1230</td>
+      <td>0.0614</td>
+      <td>0.0286</td>
+      <td>0.1341</td>
+    </tr>
+    <tr>
+      <th>96</th>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.8600</td>
+      <td>0.0400</td>
+      <td>0.0100</td>
+      <td>0.0900</td>
+      <td>0.6797</td>
+      <td>0.3202</td>
+      <td>0.0000</td>
+      <td>0.0001</td>
+      <td>0.1401</td>
+      <td>2.0120</td>
+      <td>2.0120</td>
+      <td>0.4073</td>
+      <td>0.2009</td>
+      <td>0.0282</td>
+      <td>0.1907</td>
+    </tr>
+    <tr>
+      <th>97</th>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.8500</td>
+      <td>0.0500</td>
+      <td>0.0000</td>
+      <td>0.1000</td>
+      <td>0.7974</td>
+      <td>0.1166</td>
+      <td>0.0000</td>
+      <td>0.0859</td>
+      <td>0.0333</td>
+      <td>0.3517</td>
+      <td>0.3517</td>
+      <td>0.0257</td>
+      <td>0.0128</td>
+      <td>0.0286</td>
+      <td>0.0681</td>
+    </tr>
+    <tr>
+      <th>98</th>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.8600</td>
+      <td>0.0400</td>
+      <td>0.0100</td>
+      <td>0.0900</td>
+      <td>0.7810</td>
+      <td>0.2040</td>
+      <td>0.0001</td>
+      <td>0.0149</td>
+      <td>0.0820</td>
+      <td>1.2967</td>
+      <td>1.2967</td>
+      <td>0.1783</td>
+      <td>0.0889</td>
+      <td>0.0282</td>
+      <td>0.1155</td>
+    </tr>
+    <tr>
+      <th>99</th>
+      <td>0.9000</td>
+      <td>0.1000</td>
+      <td>0.8400</td>
+      <td>0.0600</td>
+      <td>0.0000</td>
+      <td>0.1000</td>
+      <td>0.7152</td>
+      <td>0.1607</td>
+      <td>0.0000</td>
+      <td>0.1242</td>
+      <td>0.0624</td>
+      <td>0.4817</td>
+      <td>0.4817</td>
+      <td>0.0525</td>
+      <td>0.0263</td>
+      <td>0.0345</td>
+      <td>0.1010</td>
+    </tr>
+    <tr>
+      <th>100</th>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.9300</td>
+      <td>0.0700</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.9565</td>
+      <td>0.0229</td>
+      <td>0.0000</td>
+      <td>0.0206</td>
+      <td>0.0236</td>
+      <td>1.1924</td>
+      <td>1.1924</td>
+      <td>0.0399</td>
+      <td>0.0199</td>
+      <td>0.0363</td>
+      <td>0.0118</td>
+    </tr>
+    <tr>
+      <th>101</th>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.9400</td>
+      <td>0.0600</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.8954</td>
+      <td>0.0736</td>
+      <td>0.0000</td>
+      <td>0.0310</td>
+      <td>0.0223</td>
+      <td>1.6128</td>
+      <td>1.6128</td>
+      <td>0.0234</td>
+      <td>0.0117</td>
+      <td>0.0309</td>
+      <td>0.0395</td>
+    </tr>
+    <tr>
+      <th>102</th>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.9800</td>
+      <td>0.0200</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.9486</td>
+      <td>0.0514</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.0157</td>
+      <td>0.3220</td>
+      <td>0.3220</td>
+      <td>0.0115</td>
+      <td>0.0058</td>
+      <td>0.0101</td>
+      <td>0.0264</td>
+    </tr>
+    <tr>
+      <th>103</th>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.9400</td>
+      <td>0.0600</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.9317</td>
+      <td>0.0628</td>
+      <td>0.0000</td>
+      <td>0.0054</td>
+      <td>0.0041</td>
+      <td>0.2842</td>
+      <td>0.2842</td>
+      <td>0.0018</td>
+      <td>0.0009</td>
+      <td>0.0309</td>
+      <td>0.0326</td>
+    </tr>
+    <tr>
+      <th>104</th>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.9700</td>
+      <td>0.0300</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.7735</td>
+      <td>0.2265</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.0982</td>
+      <td>1.4540</td>
+      <td>1.4540</td>
+      <td>0.1533</td>
+      <td>0.0765</td>
+      <td>0.0152</td>
+      <td>0.1277</td>
+    </tr>
+    <tr>
+      <th>105</th>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.9300</td>
+      <td>0.0700</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.8494</td>
+      <td>0.1275</td>
+      <td>0.0000</td>
+      <td>0.0231</td>
+      <td>0.0403</td>
+      <td>1.3676</td>
+      <td>1.3676</td>
+      <td>0.0330</td>
+      <td>0.0165</td>
+      <td>0.0363</td>
+      <td>0.0698</td>
+    </tr>
+    <tr>
+      <th>106</th>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.9500</td>
+      <td>0.0500</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.9735</td>
+      <td>0.0148</td>
+      <td>0.0001</td>
+      <td>0.0116</td>
+      <td>0.0176</td>
+      <td>0.7536</td>
+      <td>0.7536</td>
+      <td>0.0269</td>
+      <td>0.0135</td>
+      <td>0.0256</td>
+      <td>0.0076</td>
+    </tr>
+    <tr>
+      <th>107</th>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.9400</td>
+      <td>0.0600</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.9179</td>
+      <td>0.0492</td>
+      <td>0.0000</td>
+      <td>0.0329</td>
+      <td>0.0165</td>
+      <td>1.6944</td>
+      <td>1.6944</td>
+      <td>0.0241</td>
+      <td>0.0120</td>
+      <td>0.0309</td>
+      <td>0.0261</td>
+    </tr>
+    <tr>
+      <th>108</th>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.9600</td>
+      <td>0.0400</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.9019</td>
+      <td>0.0981</td>
+      <td>0.0001</td>
+      <td>0.0000</td>
+      <td>0.0291</td>
+      <td>0.3405</td>
+      <td>0.3405</td>
+      <td>0.0226</td>
+      <td>0.0113</td>
+      <td>0.0204</td>
+      <td>0.0516</td>
+    </tr>
+    <tr>
+      <th>109</th>
+      <td>1.0000</td>
+      <td>0.0000</td>
+      <td>0.9400</td>
+      <td>0.0600</td>
+      <td>0.0000</td>
+      <td>0.0000</td>
+      <td>0.9443</td>
+      <td>0.0236</td>
+      <td>0.0003</td>
+      <td>0.0317</td>
+      <td>0.0182</td>
+      <td>1.7433</td>
+      <td>1.7433</td>
+      <td>0.0387</td>
+      <td>0.0194</td>
+      <td>0.0309</td>
+      <td>0.0125</td>
+    </tr>
+  </tbody>
+</table>
+**************************************************
diff --git a/quacc/data.py b/quacc/data.py
index f8fad6d..715802b 100644
--- a/quacc/data.py
+++ b/quacc/data.py
@@ -1,7 +1,9 @@
+from typing import List, Optional
+
 import numpy as np
+import quapy as qp
 import scipy.sparse as sp
 from quapy.data import LabelledCollection
-from typing import List, Optional
 
 
 class ExtendedCollection(LabelledCollection):
@@ -12,3 +14,17 @@ class ExtendedCollection(LabelledCollection):
         classes: Optional[List] = None,
     ):
         super().__init__(instances, labels, classes=classes)
+        
+def get_dataset(name):
+    datasets = {
+        "spambase": lambda: qp.datasets.fetch_UCIDataset(
+            "spambase", verbose=False
+        ).train_test,
+        "hp": lambda: qp.datasets.fetch_reviews("hp", tfidf=True).train_test,
+        "imdb": lambda: qp.datasets.fetch_reviews("imdb", tfidf=True).train_test,
+    }
+
+    try:
+        return datasets[name]()
+    except KeyError:
+        raise KeyError(f"{name} is not available as a dataset")
diff --git a/quacc/evaluation.py b/quacc/evaluation.py
index d12d098..029d476 100644
--- a/quacc/evaluation.py
+++ b/quacc/evaluation.py
@@ -1,16 +1,20 @@
+import itertools
 from quapy.protocol import (
     OnLabelledCollectionProtocol,
     AbstractStochasticSeededProtocol,
 )
-import quapy as qp
 from typing import Iterable, Callable, Union
 
 from .estimator import AccuracyEstimator
 import pandas as pd
+import numpy as np
 import quacc.error as error
 
 
-def estimate(estimator: AccuracyEstimator, protocol: AbstractStochasticSeededProtocol):
+def estimate(
+    estimator: AccuracyEstimator,
+    protocol: AbstractStochasticSeededProtocol,
+):
     # ensure that the protocol returns a LabelledCollection for each iteration
     protocol.collator = OnLabelledCollectionProtocol.get_collator("labelled_collection")
 
@@ -18,6 +22,9 @@ def estimate(estimator: AccuracyEstimator, protocol: AbstractStochasticSeededPro
     for sample in protocol():
         e_sample = estimator.extend(sample)
         estim_prev = estimator.estimate(e_sample.X, ext=True)
+        # base_prevs.append(_prettyfloat(accuracy, sample.prevalence()))
+        # true_prevs.append(_prettyfloat(accuracy, e_sample.prevalence()))
+        # estim_prevs.append(_prettyfloat(accuracy, estim_prev))
         base_prevs.append(sample.prevalence())
         true_prevs.append(e_sample.prevalence())
         estim_prevs.append(estim_prev)
@@ -25,6 +32,38 @@ def estimate(estimator: AccuracyEstimator, protocol: AbstractStochasticSeededPro
     return base_prevs, true_prevs, estim_prevs
 
 
+_bprev_col_0 = ["base"]
+_bprev_col_1 = ["0", "1"]
+_prev_col_0 = ["true", "estim"]
+_prev_col_1 = ["T0", "F1", "F0", "T1"]
+_err_col_0 = ["errors"]
+
+
+def _report_columns(err_names):
+    bprev_cols = list(itertools.product(_bprev_col_0, _bprev_col_1))
+    prev_cols = list(itertools.product(_prev_col_0, _prev_col_1))
+
+    err_1 = err_names
+    err_cols = list(itertools.product(_err_col_0, err_1))
+
+    cols = bprev_cols + prev_cols + err_cols
+
+    return pd.MultiIndex.from_tuples(cols)
+
+
+def _dict_prev(base_prev, true_prev, estim_prev):
+    prev_cols = list(itertools.product(_bprev_col_0, _bprev_col_1)) + list(
+        itertools.product(_prev_col_0, _prev_col_1)
+    )
+
+    return {
+        k: v
+        for (k, v) in zip(
+            prev_cols, np.concatenate((base_prev, true_prev, estim_prev), axis=0)
+        )
+    }
+
+
 def evaluation_report(
     estimator: AccuracyEstimator,
     protocol: AbstractStochasticSeededProtocol,
@@ -40,26 +79,25 @@ def evaluation_report(
     ]
     assert all(hasattr(e, "__call__") for e in error_funcs), "invalid error function"
     error_names = [e.__name__ for e in error_funcs]
+    error_cols = error_names.copy()
+    if "f1e" in error_cols:
+        error_cols.remove("f1e")
+        error_cols.extend(["f1e_true", "f1e_estim"])
+
+    # df_cols = ["base_prev", "true_prev", "estim_prev"] + error_names
+    df_cols = _report_columns(error_cols)
 
-    df_cols = ["base_prev", "true_prev", "estim_prev"] + error_names
-    if "f1e" in df_cols:
-        df_cols.remove("f1e")
-        df_cols.extend(["f1e_true", "f1e_estim"])
     lst = []
     for base_prev, true_prev, estim_prev in zip(base_prevs, true_prevs, estim_prevs):
-        series = {
-            "base_prev": base_prev,
-            "true_prev": true_prev,
-            "estim_prev": estim_prev,
-        }
+        series = _dict_prev(base_prev, true_prev, estim_prev)
         for error_name, error_metric in zip(error_names, error_funcs):
             if error_name == "f1e":
-                series["f1e_true"] = error_metric(true_prev)
-                series["f1e_estim"] = error_metric(estim_prev)
+                series[("errors", "f1e_true")] = error_metric(true_prev)
+                series[("errors", "f1e_estim")] = error_metric(estim_prev)
                 continue
 
             score = error_metric(true_prev, estim_prev)
-            series[error_name] = score
+            series[("errors", error_name)] = score
 
         lst.append(series)
 
diff --git a/quacc/main.py b/quacc/main.py
index 51f4b04..14fa7d0 100644
--- a/quacc/main.py
+++ b/quacc/main.py
@@ -1,158 +1,17 @@
-import numpy as np
+import pandas as pd
 import quapy as qp
-import scipy.sparse as sp
-from quapy.data import LabelledCollection
 from quapy.method.aggregative import SLD
-from quapy.protocol import APP, AbstractStochasticSeededProtocol
+from quapy.protocol import APP
 from sklearn.linear_model import LogisticRegression
-from sklearn.model_selection import cross_val_predict
 
 import quacc.evaluation as eval
 from quacc.estimator import AccuracyEstimator
 
-qp.environ['SAMPLE_SIZE'] = 100
+from .data import get_dataset
 
+qp.environ["SAMPLE_SIZE"] = 100
 
-# Extended classes
-#
-# 0 ~ True 0
-# 1 ~ False 1
-# 2 ~ False 0
-# 3 ~ True 1
-#      _____________________
-#     |          |          |
-#     |  True 0  |  False 1 |
-#     |__________|__________|
-#     |          |          |
-#     |  False 0 |  True 1  |
-#     |__________|__________|
-#
-def get_ex_class(classes, true_class, pred_class):
-    return true_class * classes + pred_class
-
-
-def extend_collection(coll, pred_prob):
-    n_classes = coll.n_classes
-
-    # n_X = [ X | predicted probs. ]
-    if isinstance(coll.X, sp.csr_matrix):
-        pred_prob_csr = sp.csr_matrix(pred_prob)
-        n_x = sp.hstack([coll.X, pred_prob_csr])
-    elif isinstance(coll.X, np.ndarray):
-        n_x = np.concatenate((coll.X, pred_prob), axis=1)
-    else:
-        raise ValueError("Unsupported matrix format")
-
-    # n_y = (exptected y, predicted y)
-    n_y = []
-    for i, true_class in enumerate(coll.y):
-        pred_class = pred_prob[i].argmax(axis=0)
-        n_y.append(get_ex_class(n_classes, true_class, pred_class))
-
-    return LabelledCollection(n_x, np.asarray(n_y), [*range(0, n_classes * n_classes)])
-
-
-def qf1e_binary(prev):
-    recall = prev[0] / (prev[0] + prev[1])
-    precision = prev[0] / (prev[0] + prev[2])
-
-    return 1 - 2 * (precision * recall) / (precision + recall)
-
-
-def compute_errors(true_prev, estim_prev, n_instances):
-    errors = {}
-    _eps = 1 / (2 * n_instances)
-    errors = {
-        "mae": qp.error.mae(true_prev, estim_prev),
-        "rae": qp.error.rae(true_prev, estim_prev, eps=_eps),
-        "mrae": qp.error.mrae(true_prev, estim_prev, eps=_eps),
-        "kld": qp.error.kld(true_prev, estim_prev, eps=_eps),
-        "nkld": qp.error.nkld(true_prev, estim_prev, eps=_eps),
-        "true_f1e": qf1e_binary(true_prev),
-        "estim_f1e": qf1e_binary(estim_prev),
-    }
-
-    return errors
-
-
-def extend_and_quantify(
-    model,
-    q_model,
-    train,
-    test: LabelledCollection | AbstractStochasticSeededProtocol,
-):
-    model.fit(*train.Xy)
-
-    pred_prob_train = cross_val_predict(model, *train.Xy, method="predict_proba")
-    _train = extend_collection(train, pred_prob_train)
-
-    q_model.fit(_train)
-
-    def quantify_extended(test):
-        pred_prob_test = model.predict_proba(test.X)
-        _test = extend_collection(test, pred_prob_test)
-        _estim_prev = q_model.quantify(_test.instances)
-        # check that _estim_prev has all the classes and eventually fill the missing
-        # ones with 0
-        for _cls in _test.classes_:
-            if _cls not in q_model.classes_:
-                _estim_prev = np.insert(_estim_prev, _cls, [0.0], axis=0)
-                print(_estim_prev)
-        return _test.prevalence(), _estim_prev
-
-    if isinstance(test, LabelledCollection):
-        _true_prev, _estim_prev = quantify_extended(test)
-        _errors = compute_errors(_true_prev, _estim_prev, test.X.shape[0])
-        return ([test.prevalence()], [_true_prev], [_estim_prev], [_errors])
-
-    elif isinstance(test, AbstractStochasticSeededProtocol):
-        orig_prevs, true_prevs, estim_prevs, errors = [], [], [], []
-        for index in test.samples_parameters():
-            sample = test.sample(index)
-            _true_prev, _estim_prev = quantify_extended(sample)
-
-            orig_prevs.append(sample.prevalence())
-            true_prevs.append(_true_prev)
-            estim_prevs.append(_estim_prev)
-            errors.append(compute_errors(_true_prev, _estim_prev, sample.X.shape[0]))
-
-        return orig_prevs, true_prevs, estim_prevs, errors
-
-
-def get_dataset(name):
-    datasets = {
-        "spambase": lambda: qp.datasets.fetch_UCIDataset(
-            "spambase", verbose=False
-        ).train_test,
-        "hp": lambda: qp.datasets.fetch_reviews("hp", tfidf=True).train_test,
-        "imdb": lambda: qp.datasets.fetch_reviews("imdb", tfidf=True).train_test,
-    }
-
-    try:
-        return datasets[name]()
-    except KeyError:
-        raise KeyError(f"{name} is not available as a dataset")
-
-
-def test_1(dataset_name):
-    train, test = get_dataset(dataset_name)
-
-    orig_prevs, true_prevs, estim_prevs, errors = extend_and_quantify(
-        LogisticRegression(),
-        SLD(LogisticRegression()),
-        train,
-        APP(test, n_prevalences=11, repeats=1),
-    )
-
-    for orig_prev, true_prev, estim_prev, _errors in zip(
-        orig_prevs, true_prevs, estim_prevs, errors
-    ):
-        print(f"original prevalence:\t{orig_prev}")
-        print(f"true prevalence:\t{true_prev}")
-        print(f"estimated prevalence:\t{estim_prev}")
-        for name, err in _errors.items():
-            print(f"{name}={err:.3f}")
-        print()
+pd.set_option("display.float_format", "{:.4f}".format)
 
 
 def test_2(dataset_name):
@@ -161,9 +20,8 @@ def test_2(dataset_name):
     model.fit(*train.Xy)
     estimator = AccuracyEstimator(model, SLD(LogisticRegression()))
     estimator.fit(train)
-    df = eval.evaluation_report(
-        estimator, APP(test, n_prevalences=11, repeats=1)
-    )
+    df = eval.evaluation_report(estimator, APP(test, n_prevalences=11, repeats=100))
+    # print(df.to_string())
     print(df.to_string())
 
 
diff --git a/quacc/test_1.py b/quacc/test_1.py
new file mode 100644
index 0000000..00ea263
--- /dev/null
+++ b/quacc/test_1.py
@@ -0,0 +1,138 @@
+import numpy as np
+import scipy as sp
+import quapy as qp
+from quapy.data import LabelledCollection
+from quapy.method.aggregative import SLD
+from quapy.protocol import APP, AbstractStochasticSeededProtocol
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import cross_val_predict
+
+from .data import get_dataset
+
+# Extended classes
+#
+# 0 ~ True 0
+# 1 ~ False 1
+# 2 ~ False 0
+# 3 ~ True 1
+#      _____________________
+#     |          |          |
+#     |  True 0  |  False 1 |
+#     |__________|__________|
+#     |          |          |
+#     |  False 0 |  True 1  |
+#     |__________|__________|
+#
+def get_ex_class(classes, true_class, pred_class):
+    return true_class * classes + pred_class
+
+
+def extend_collection(coll, pred_prob):
+    n_classes = coll.n_classes
+
+    # n_X = [ X | predicted probs. ]
+    if isinstance(coll.X, sp.csr_matrix):
+        pred_prob_csr = sp.csr_matrix(pred_prob)
+        n_x = sp.hstack([coll.X, pred_prob_csr])
+    elif isinstance(coll.X, np.ndarray):
+        n_x = np.concatenate((coll.X, pred_prob), axis=1)
+    else:
+        raise ValueError("Unsupported matrix format")
+
+    # n_y = (exptected y, predicted y)
+    n_y = []
+    for i, true_class in enumerate(coll.y):
+        pred_class = pred_prob[i].argmax(axis=0)
+        n_y.append(get_ex_class(n_classes, true_class, pred_class))
+
+    return LabelledCollection(n_x, np.asarray(n_y), [*range(0, n_classes * n_classes)])
+
+
+def qf1e_binary(prev):
+    recall = prev[0] / (prev[0] + prev[1])
+    precision = prev[0] / (prev[0] + prev[2])
+
+    return 1 - 2 * (precision * recall) / (precision + recall)
+
+
+def compute_errors(true_prev, estim_prev, n_instances):
+    errors = {}
+    _eps = 1 / (2 * n_instances)
+    errors = {
+        "mae": qp.error.mae(true_prev, estim_prev),
+        "rae": qp.error.rae(true_prev, estim_prev, eps=_eps),
+        "mrae": qp.error.mrae(true_prev, estim_prev, eps=_eps),
+        "kld": qp.error.kld(true_prev, estim_prev, eps=_eps),
+        "nkld": qp.error.nkld(true_prev, estim_prev, eps=_eps),
+        "true_f1e": qf1e_binary(true_prev),
+        "estim_f1e": qf1e_binary(estim_prev),
+    }
+
+    return errors
+
+
+def extend_and_quantify(
+    model,
+    q_model,
+    train,
+    test: LabelledCollection | AbstractStochasticSeededProtocol,
+):
+    model.fit(*train.Xy)
+
+    pred_prob_train = cross_val_predict(model, *train.Xy, method="predict_proba")
+    _train = extend_collection(train, pred_prob_train)
+
+    q_model.fit(_train)
+
+    def quantify_extended(test):
+        pred_prob_test = model.predict_proba(test.X)
+        _test = extend_collection(test, pred_prob_test)
+        _estim_prev = q_model.quantify(_test.instances)
+        # check that _estim_prev has all the classes and eventually fill the missing
+        # ones with 0
+        for _cls in _test.classes_:
+            if _cls not in q_model.classes_:
+                _estim_prev = np.insert(_estim_prev, _cls, [0.0], axis=0)
+                print(_estim_prev)
+        return _test.prevalence(), _estim_prev
+
+    if isinstance(test, LabelledCollection):
+        _true_prev, _estim_prev = quantify_extended(test)
+        _errors = compute_errors(_true_prev, _estim_prev, test.X.shape[0])
+        return ([test.prevalence()], [_true_prev], [_estim_prev], [_errors])
+
+    elif isinstance(test, AbstractStochasticSeededProtocol):
+        orig_prevs, true_prevs, estim_prevs, errors = [], [], [], []
+        for index in test.samples_parameters():
+            sample = test.sample(index)
+            _true_prev, _estim_prev = quantify_extended(sample)
+
+            orig_prevs.append(sample.prevalence())
+            true_prevs.append(_true_prev)
+            estim_prevs.append(_estim_prev)
+            errors.append(compute_errors(_true_prev, _estim_prev, sample.X.shape[0]))
+
+        return orig_prevs, true_prevs, estim_prevs, errors
+
+
+
+
+def test_1(dataset_name):
+    train, test = get_dataset(dataset_name)
+
+    orig_prevs, true_prevs, estim_prevs, errors = extend_and_quantify(
+        LogisticRegression(),
+        SLD(LogisticRegression()),
+        train,
+        APP(test, n_prevalences=11, repeats=1),
+    )
+
+    for orig_prev, true_prev, estim_prev, _errors in zip(
+        orig_prevs, true_prevs, estim_prevs, errors
+    ):
+        print(f"original prevalence:\t{orig_prev}")
+        print(f"true prevalence:\t{true_prev}")
+        print(f"estimated prevalence:\t{estim_prev}")
+        for name, err in _errors.items():
+            print(f"{name}={err:.3f}")
+        print()