From 62c69e14f182dc0fe14f0f182203dca886cca560 Mon Sep 17 00:00:00 2001
From: JesperDramsch <JesperDramsch@users.noreply.github.com>
Date: Sun, 11 Feb 2024 14:54:00 +0000
Subject: [PATCH] deploy: 175959d0c9e8ba7594ddf125ef01a3ecfb96ae88

---
 ...8c467b5c2efb5382dcf3f246cc29c208139e6e.png | Bin 0 -> 20671 bytes
 ...51a2aed2125b4909a8b506c8ed2136a2bc13d2.png | Bin 0 -> 18246 bytes
 ...91ff1d1f709d050d2aefc911ebec8a74b404e3.png | Bin 17124 -> 0 bytes
 ...3d395e2e4c3f0e6bc69ce6ee11d573526c9fb5.png | Bin 16077 -> 0 bytes
 ...657fa94da63b2d1a7297eabf6c38fd95e9053e.png | Bin 0 -> 16060 bytes
 ...1409d2fc81bfe6ca226484623cf1e93a5284bf.png | Bin 0 -> 16040 bytes
 ...3579567d53f9d44ec71b781d036709a32b4587.png | Bin 16022 -> 0 bytes
 _sources/notebooks/5-interpretability.ipynb   |  44 +++++--
 notebooks/0-basic-data-prep-and-model.html    | 122 +++++++++---------
 notebooks/1-model-evaluation.html             |   4 +-
 notebooks/5-interpretability.html             | 116 +++++++----------
 searchindex.js                                |   2 +-
 12 files changed, 144 insertions(+), 144 deletions(-)
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 create mode 100644 _images/4d9c80cb801cc13c75abdec0fa51a2aed2125b4909a8b506c8ed2136a2bc13d2.png
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 delete mode 100644 _images/986d3355a04a331a4f82450cdb3d395e2e4c3f0e6bc69ce6ee11d573526c9fb5.png
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diff --git a/_sources/notebooks/5-interpretability.ipynb b/_sources/notebooks/5-interpretability.ipynb
index 5ad92c5..f6ddf9a 100644
--- a/_sources/notebooks/5-interpretability.ipynb
+++ b/_sources/notebooks/5-interpretability.ipynb
@@ -270,9 +270,10 @@
     "from sklearn.inspection import PartialDependenceDisplay, partial_dependence\n",
     "from matplotlib import pyplot as plt\n",
     "\n",
+    "\n",
     "pd_results = partial_dependence(model, X_train.sample(20), num_features)\n",
     "print(pd_results.keys())\n",
-    "print(f\"Example Values: {pd_results['values'][0]}, Average: {pd_results['average'][0].mean(axis=0)}\")"
+    "print(f\"Example Values: {pd_results['values'][0]}, Average: {pd_results['average'][0][0].mean(axis=0)}\")"
    ]
   },
   {
@@ -467,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2022-12-13T01:42:23.343022Z",
@@ -490,14 +491,22 @@
     }
    ],
    "source": [
-    "pd.Series(rf.named_steps[\"classifier\"].feature_importances_, index=num_features+['F', 'M']).plot.bar()\n",
+    "pd.Series(rf.named_steps[\"classifier\"].feature_importances_, index=num_features+['Female', 'Male']).plot.bar()\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "The tree-based feature importance shows the importances as the \"random forest sees them\", which means we get the `Sex` feature split into male and female from the OneHotEncoding. This also means that this categorical features is correlated strongly.\n",
+    "\n",
+    "We can clearly see that the `Culmen length` is the most important feature in determining which penguin we're facing. `Culmen depth` seems to be slightly less important than `Flipper length`. `Sex` seems to be entirely unimportant.\n",
+    "\n",
+    "Now we can use the more sophisticated permutation importance. \n",
+    "\n",
+    "Luckily, scikit-learn implements this feature for us and we can just import it:"
+   ]
   },
   {
    "cell_type": "code",
@@ -527,13 +536,19 @@
     "from sklearn.inspection import permutation_importance\n",
     "\n",
     "result = permutation_importance(\n",
-    "    rf, X_test, y_test, n_repeats=10, random_state=42\n",
+    "    rf, X_train, y_train, n_repeats=10, random_state=42\n",
     ")\n",
     "\n",
-    "pd.Series(result.importances_mean, index=features).plot.bar()\n",
+    "fi_rf_train = pd.Series(result.importances_mean, index=features)\n",
+    "fi_rf_train.plot.bar()\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 12,
@@ -560,13 +575,26 @@
    ],
    "source": [
     "result = permutation_importance(\n",
-    "    model, X_test, y_test, n_repeats=10, random_state=42\n",
+    "    model, X_train, y_train, n_repeats=10, random_state=42\n",
     ")\n",
     "\n",
-    "pd.Series(result.importances_mean, index=features).plot.bar()\n",
+    "fi_svm_train = pd.Series(result.importances_mean, index=features)\n",
+    "fi_svm_train.plot.bar()\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fi_rf_test = pd.Series(permutation_importance(rf, X_test, y_test, n_repeats=10, random_state=42), index=features)\n",
+    "fi_svm_test = pd.Series(permutation_importance(model, X_test, y_test, n_repeats=10, random_state=42), index=features)\n",
+    "\n",
+    "pd.DataFrame({\"RF Train\": fi_rf_train, \"SVM Train\": fi_svm_train, \"RF Test\": fi_rf_test, \"SVM Test\": fi_svm_test}).plot.bar()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/notebooks/0-basic-data-prep-and-model.html b/notebooks/0-basic-data-prep-and-model.html
index b69f323..18419ef 100644
--- a/notebooks/0-basic-data-prep-and-model.html
+++ b/notebooks/0-basic-data-prep-and-model.html
@@ -984,38 +984,38 @@ <h2>Machine Learning<a class="headerlink" href="#machine-learning" title="Permal
   </thead>
   <tbody>
     <tr>
-      <th>38</th>
-      <td>37.6</td>
-      <td>19.3</td>
-      <td>181.0</td>
+      <th>34</th>
+      <td>36.4</td>
+      <td>17.0</td>
+      <td>195.0</td>
       <td>FEMALE</td>
     </tr>
     <tr>
-      <th>93</th>
-      <td>39.6</td>
-      <td>18.1</td>
-      <td>186.0</td>
+      <th>243</th>
+      <td>52.2</td>
+      <td>17.1</td>
+      <td>228.0</td>
       <td>MALE</td>
     </tr>
     <tr>
-      <th>152</th>
-      <td>46.1</td>
-      <td>13.2</td>
-      <td>211.0</td>
+      <th>312</th>
+      <td>47.6</td>
+      <td>18.3</td>
+      <td>195.0</td>
       <td>FEMALE</td>
     </tr>
     <tr>
-      <th>209</th>
-      <td>45.5</td>
-      <td>15.0</td>
-      <td>220.0</td>
+      <th>285</th>
+      <td>51.3</td>
+      <td>19.9</td>
+      <td>198.0</td>
       <td>MALE</td>
     </tr>
     <tr>
-      <th>161</th>
-      <td>46.8</td>
-      <td>15.4</td>
-      <td>215.0</td>
+      <th>46</th>
+      <td>41.1</td>
+      <td>19.0</td>
+      <td>182.0</td>
       <td>MALE</td>
     </tr>
     <tr>
@@ -1026,39 +1026,39 @@ <h2>Machine Learning<a class="headerlink" href="#machine-learning" title="Permal
       <td>...</td>
     </tr>
     <tr>
-      <th>91</th>
-      <td>41.1</td>
-      <td>18.1</td>
-      <td>205.0</td>
-      <td>MALE</td>
+      <th>44</th>
+      <td>37.0</td>
+      <td>16.9</td>
+      <td>185.0</td>
+      <td>FEMALE</td>
     </tr>
     <tr>
-      <th>183</th>
-      <td>42.8</td>
-      <td>14.2</td>
-      <td>209.0</td>
+      <th>236</th>
+      <td>44.9</td>
+      <td>13.8</td>
+      <td>212.0</td>
       <td>FEMALE</td>
     </tr>
     <tr>
-      <th>286</th>
-      <td>46.6</td>
-      <td>17.8</td>
+      <th>83</th>
+      <td>35.1</td>
+      <td>19.4</td>
       <td>193.0</td>
-      <td>FEMALE</td>
+      <td>MALE</td>
     </tr>
     <tr>
-      <th>337</th>
-      <td>46.8</td>
-      <td>16.5</td>
-      <td>189.0</td>
-      <td>FEMALE</td>
+      <th>31</th>
+      <td>37.2</td>
+      <td>18.1</td>
+      <td>178.0</td>
+      <td>MALE</td>
     </tr>
     <tr>
-      <th>330</th>
-      <td>42.5</td>
-      <td>17.3</td>
-      <td>187.0</td>
-      <td>FEMALE</td>
+      <th>121</th>
+      <td>37.7</td>
+      <td>19.8</td>
+      <td>198.0</td>
+      <td>MALE</td>
     </tr>
   </tbody>
 </table>
@@ -1095,48 +1095,48 @@ <h2>Machine Learning<a class="headerlink" href="#machine-learning" title="Permal
   </thead>
   <tbody>
     <tr>
-      <th>38</th>
+      <th>34</th>
       <td>Adelie Penguin (Pygoscelis adeliae)</td>
     </tr>
     <tr>
-      <th>93</th>
-      <td>Adelie Penguin (Pygoscelis adeliae)</td>
+      <th>243</th>
+      <td>Gentoo penguin (Pygoscelis papua)</td>
     </tr>
     <tr>
-      <th>152</th>
-      <td>Gentoo penguin (Pygoscelis papua)</td>
+      <th>312</th>
+      <td>Chinstrap penguin (Pygoscelis antarctica)</td>
     </tr>
     <tr>
-      <th>209</th>
-      <td>Gentoo penguin (Pygoscelis papua)</td>
+      <th>285</th>
+      <td>Chinstrap penguin (Pygoscelis antarctica)</td>
     </tr>
     <tr>
-      <th>161</th>
-      <td>Gentoo penguin (Pygoscelis papua)</td>
+      <th>46</th>
+      <td>Adelie Penguin (Pygoscelis adeliae)</td>
     </tr>
     <tr>
       <th>...</th>
       <td>...</td>
     </tr>
     <tr>
-      <th>91</th>
+      <th>44</th>
       <td>Adelie Penguin (Pygoscelis adeliae)</td>
     </tr>
     <tr>
-      <th>183</th>
+      <th>236</th>
       <td>Gentoo penguin (Pygoscelis papua)</td>
     </tr>
     <tr>
-      <th>286</th>
-      <td>Chinstrap penguin (Pygoscelis antarctica)</td>
+      <th>83</th>
+      <td>Adelie Penguin (Pygoscelis adeliae)</td>
     </tr>
     <tr>
-      <th>337</th>
-      <td>Chinstrap penguin (Pygoscelis antarctica)</td>
+      <th>31</th>
+      <td>Adelie Penguin (Pygoscelis adeliae)</td>
     </tr>
     <tr>
-      <th>330</th>
-      <td>Chinstrap penguin (Pygoscelis antarctica)</td>
+      <th>121</th>
+      <td>Adelie Penguin (Pygoscelis adeliae)</td>
     </tr>
   </tbody>
 </table>
@@ -1278,7 +1278,7 @@ <h3>Model Training<a class="headerlink" href="#model-training" title="Permalink
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9900990099009901
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9801980198019802
 </pre></div>
 </div>
 </div>
diff --git a/notebooks/1-model-evaluation.html b/notebooks/1-model-evaluation.html
index c31df0f..51e0307 100644
--- a/notebooks/1-model-evaluation.html
+++ b/notebooks/1-model-evaluation.html
@@ -1146,8 +1146,8 @@ <h2><span class="section-number">1.3.5. </span>Choosing the appropriate Evaluati
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;fit_time&#39;: array([0.00581956, 0.00523829, 0.00519466, 0.00517082, 0.00550675]),
- &#39;score_time&#39;: array([0.00410533, 0.00397706, 0.00397515, 0.00400424, 0.00418091]),
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;fit_time&#39;: array([0.00599885, 0.00516748, 0.00515461, 0.00513673, 0.00516844]),
+ &#39;score_time&#39;: array([0.00435352, 0.00395489, 0.00400567, 0.00393963, 0.00396037]),
  &#39;test_MCC&#39;: array([0.37796447, 0.27863911, 0.40824829, 0.02424643, 0.08625819]),
  &#39;test_ACC&#39;: array([0.73333333, 0.7       , 0.76666667, 0.66666667, 0.62068966])}
 </pre></div>
diff --git a/notebooks/5-interpretability.html b/notebooks/5-interpretability.html
index 7c5f853..88a293e 100644
--- a/notebooks/5-interpretability.html
+++ b/notebooks/5-interpretability.html
@@ -604,69 +604,19 @@ <h2><span class="section-number">5.3.1. </span>Partial Dependence for Machine Le
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.inspection</span> <span class="kn">import</span> <span class="n">PartialDependenceDisplay</span><span class="p">,</span> <span class="n">partial_dependence</span>
 <span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
 
+
 <span class="n">pd_results</span> <span class="o">=</span> <span class="n">partial_dependence</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X_train</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">20</span><span class="p">),</span> <span class="n">num_features</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">pd_results</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Example Values: </span><span class="si">{</span><span class="n">pd_results</span><span class="p">[</span><span class="s1">&#39;values&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">, Average: </span><span class="si">{</span><span class="n">pd_results</span><span class="p">[</span><span class="s1">&#39;average&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Example Values: </span><span class="si">{</span><span class="n">pd_results</span><span class="p">[</span><span class="s1">&#39;values&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">, Average: </span><span class="si">{</span><span class="n">pd_results</span><span class="p">[</span><span class="s1">&#39;average&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>dict_keys([&#39;grid_values&#39;, &#39;values&#39;, &#39;average&#39;])
-Example Values: [36.2 37.  37.3 37.7 38.8 40.8 41.1 42.1 42.3 43.2 43.5 45.6 46.2 46.7
- 46.8 49.1 50.  50.5 50.7 51.5], Average: [[0.90177069 0.78671704 0.70469108 0.56801047 0.56387708 0.55967227
-  0.55107687 0.52167929 0.37392551 0.33493436 0.29741053 0.29468135
-  0.29041098 0.28760599 0.28612207 0.26249271]
- [0.95854922 0.84406229 0.78728123 0.67601119 0.64692182 0.6176926
-  0.60894839 0.60450488 0.45647307 0.34221175 0.30205558 0.29872462
-  0.29333685 0.28960929 0.28738673 0.28743635]
- [1.01551367 0.92684212 0.87024088 0.75932672 0.73040366 0.72631957
-  0.64263557 0.63808699 0.53973778 0.40017748 0.30797264 0.30409229
-  0.29746883 0.29261721 0.28948639 0.28796838]
- [1.21543358 1.15454241 1.04923128 0.99011567 0.98671552 0.98310429
-  0.87532583 0.87120056 0.64807017 0.60751218 0.33510354 0.33014945
-  0.3205865  0.31189322 0.30470411 0.29497534]
- [1.24402018 1.18345138 1.07825509 0.99439123 0.99110542 0.98762567
-  0.90504265 0.87597612 0.65319315 0.63762267 0.33948467 0.3343985
-  0.32463604 0.31556777 0.30788518 0.29670303]
- [1.46518415 1.25675279 1.22731435 1.21935174 1.1913854  1.11326505
-  1.03165386 1.00314301 0.88281229 0.76848444 0.49282031 0.43715282
-  0.35095601 0.34009502 0.33025273 0.31129916]
- [1.54706175 1.33922813 1.31009225 1.22762444 1.22474917 1.22169955
-  1.09012529 1.06164286 0.96704899 0.82799127 0.5776392  0.5217532
-  0.41022477 0.34899575 0.338429   0.31750116]
- [1.56949432 1.56359638 1.56033575 1.47925381 1.40187371 1.34932349
-  1.29368981 1.26557547 1.17175739 1.0084355  0.7110423  0.6549509
-  0.64240401 0.58020699 0.46824794 0.34180036]
- [1.5723779  1.56667319 1.56353138 1.50770101 1.48040632 1.40291521
-  1.32237702 1.29433087 1.17580618 1.03746466 0.74033273 0.70937585
-  0.64687068 0.60948593 0.52245194 0.34540078]
- [1.61327044 1.60801497 1.60519383 1.60013854 1.59820991 1.5961448
-  1.59155638 1.5640031  1.29780848 1.23512441 0.96313622 0.93238057
-  0.8207369  0.70862276 0.64601296 0.44171237]
- [1.61572641 1.61045503 1.60763686 1.60261566 1.60070985 1.59867496
-  1.59417395 1.5916806  1.37600032 1.23848599 0.99154717 0.93578386
-  0.84914562 0.73716793 0.67464783 0.4950705 ]
- [1.73328051 1.65290729 1.64986218 1.64448946 1.64252114 1.6404623
-  1.61105659 1.60869728 1.56992278 1.50949592 1.21442115 1.15868685
-  1.04702336 0.96022004 0.9234437  0.64376455]
- [1.73519938 1.67982643 1.6517578  1.64630121 1.64430186 1.64221505
-  1.63776544 1.61039161 1.57164816 1.51135617 1.21671527 1.21098726
-  1.07432268 0.98753812 0.9507733  0.67126142]
- [1.7637881  1.73347284 1.68035391 1.64975343 1.64768095 1.64552627
-  1.64096246 1.638545   1.57477148 1.5396688  1.22093739 1.21519319
-  1.12855512 1.01680176 0.93005867 0.75092701]
- [1.76995852 1.73979293 1.73663613 1.70588806 1.67869752 1.65139213
-  1.64651792 1.64396753 1.57984487 1.54487162 1.30285845 1.22211976
-  1.18553287 1.14884767 1.03720694 0.88385137]
- [1.77368698 1.74365561 1.74045745 1.7096149  1.70740263 1.68005246
-  1.64995792 1.64728185 1.58272349 1.54769294 1.35665257 1.32599698
-  1.21445766 1.15288791 1.14136026 0.88856036]
- [1.77647515 1.77170323 1.74349877 1.71253491 1.71028273 1.70790049
-  1.67772755 1.64993363 1.58485951 1.54968728 1.43434804 1.40375491
-  1.2673429  1.20591059 1.14452556 0.8923069 ]
- [1.79945725 1.77243788 1.76985703 1.7392254  1.73695508 1.70949457
-  1.70407438 1.70116209 1.58464611 1.5485844  1.4341855  1.42907144
-  1.41867805 1.38335043 1.32327375 1.05009869]]
+Example Values: [37.3 37.5 37.9 39.5 39.7 40.6 43.3 43.5 43.6 46.1 46.8 47.5 48.5 49.1
+ 49.2 49.6 50.  52.2], Average: [2.20487658 2.21552307 2.21479696 2.21303952 2.21025845 2.20845457
+ 2.14753287 2.08356405 1.77933171 1.71805918 1.71004992 1.69310813
+ 1.68442829 1.67569758 1.64047913 1.59938741]
 </pre></div>
 </div>
 </div>
@@ -756,45 +706,67 @@ <h3><span class="section-number">5.3.1.1. </span>Feature importances with Tree i
 <p>These will always be slightly different, due to the training process of Random Forests on randomly selected subsets of the data.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">rf</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s2">&quot;classifier&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">feature_importances_</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">num_features</span><span class="o">+</span><span class="p">[</span><span class="s1">&#39;F&#39;</span><span class="p">,</span> <span class="s1">&#39;M&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">()</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">rf</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s2">&quot;classifier&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">feature_importances_</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">num_features</span><span class="o">+</span><span class="p">[</span><span class="s1">&#39;Female&#39;</span><span class="p">,</span> <span class="s1">&#39;Male&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">()</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/5dd4146ebf31fbe53c566ab5a091ff1d1f709d050d2aefc911ebec8a74b404e3.png" src="../_images/5dd4146ebf31fbe53c566ab5a091ff1d1f709d050d2aefc911ebec8a74b404e3.png" />
+<img alt="../_images/4d9c80cb801cc13c75abdec0fa51a2aed2125b4909a8b506c8ed2136a2bc13d2.png" src="../_images/4d9c80cb801cc13c75abdec0fa51a2aed2125b4909a8b506c8ed2136a2bc13d2.png" />
 </div>
 </div>
+<p>The tree-based feature importance shows the importances as the “random forest sees them”, which means we get the <code class="docutils literal notranslate"><span class="pre">Sex</span></code> feature split into male and female from the OneHotEncoding. This also means that this categorical features is correlated strongly.</p>
+<p>We can clearly see that the <code class="docutils literal notranslate"><span class="pre">Culmen</span> <span class="pre">length</span></code> is the most important feature in determining which penguin we’re facing. <code class="docutils literal notranslate"><span class="pre">Culmen</span> <span class="pre">depth</span></code> seems to be slightly less important than <code class="docutils literal notranslate"><span class="pre">Flipper</span> <span class="pre">length</span></code>. <code class="docutils literal notranslate"><span class="pre">Sex</span></code> seems to be entirely unimportant.</p>
+<p>Now we can use the more sophisticated permutation importance.</p>
+<p>Luckily, scikit-learn implements this feature for us and we can just import it:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.inspection</span> <span class="kn">import</span> <span class="n">permutation_importance</span>
 
 <span class="n">result</span> <span class="o">=</span> <span class="n">permutation_importance</span><span class="p">(</span>
-    <span class="n">rf</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">n_repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
+    <span class="n">rf</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">n_repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
 <span class="p">)</span>
 
-<span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">importances_mean</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">features</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">()</span>
+<span class="n">fi_rf_train</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">importances_mean</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">features</span><span class="p">)</span>
+<span class="n">fi_rf_train</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">()</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/986d3355a04a331a4f82450cdb3d395e2e4c3f0e6bc69ce6ee11d573526c9fb5.png" src="../_images/986d3355a04a331a4f82450cdb3d395e2e4c3f0e6bc69ce6ee11d573526c9fb5.png" />
+<img alt="../_images/ef0beddf67764f8f44cb60e7f2657fa94da63b2d1a7297eabf6c38fd95e9053e.png" src="../_images/ef0beddf67764f8f44cb60e7f2657fa94da63b2d1a7297eabf6c38fd95e9053e.png" />
 </div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">permutation_importance</span><span class="p">(</span>
-    <span class="n">model</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">n_repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
+    <span class="n">model</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">n_repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
 <span class="p">)</span>
 
-<span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">importances_mean</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">features</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">()</span>
+<span class="n">fi_svm_train</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">importances_mean</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">features</span><span class="p">)</span>
+<span class="n">fi_svm_train</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">()</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/f408cd7c3d2fd283ba309ea8c13579567d53f9d44ec71b781d036709a32b4587.png" src="../_images/f408cd7c3d2fd283ba309ea8c13579567d53f9d44ec71b781d036709a32b4587.png" />
+<img alt="../_images/ef2b25a1ae4803b6969da817341409d2fc81bfe6ca226484623cf1e93a5284bf.png" src="../_images/ef2b25a1ae4803b6969da817341409d2fc81bfe6ca226484623cf1e93a5284bf.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fi_rf_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">permutation_importance</span><span class="p">(</span><span class="n">rf</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">n_repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="n">features</span><span class="p">)</span>
+<span class="n">fi_svm_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">permutation_importance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">n_repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="n">features</span><span class="p">)</span>
+
+<span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;RF Train&quot;</span><span class="p">:</span> <span class="n">fi_rf_train</span><span class="p">,</span> <span class="s2">&quot;SVM Train&quot;</span><span class="p">:</span> <span class="n">fi_svm_train</span><span class="p">,</span> <span class="s2">&quot;RF Test&quot;</span><span class="p">:</span> <span class="n">fi_rf_test</span><span class="p">,</span> <span class="s2">&quot;SVM Test&quot;</span><span class="p">:</span> <span class="n">fi_svm_test</span><span class="p">})</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Axes: &gt;
+</pre></div>
+</div>
+<img alt="../_images/16f87ceed29993be6fd6d67e6d8c467b5c2efb5382dcf3f246cc29c208139e6e.png" src="../_images/16f87ceed29993be6fd6d67e6d8c467b5c2efb5382dcf3f246cc29c208139e6e.png" />
 </div>
 </div>
 </section>
@@ -814,7 +786,7 @@ <h2><span class="section-number">5.3.2. </span>Shap Inspection<a class="headerli
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;shap.explainers._tree.TreeExplainer at 0x7f7772b66eb0&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;shap.explainers._tree.TreeExplainer at 0x7fcd589362b0&gt;
 </pre></div>
 </div>
 </div>
@@ -845,7 +817,7 @@ <h2><span class="section-number">5.3.2. </span>Shap Inspection<a class="headerli
 </div>
 <div class="cell_output docutils container">
 <div class="output text_html">
-<div id='iB0Q2S6VO4KTOFDZM68UF'>
+<div id='iCFYKW8KIPBJHFNICBCAO'>
 <div style='color: #900; text-align: center;'>
   <b>Visualization omitted, Javascript library not loaded!</b><br>
   Have you run `initjs()` in this notebook? If this notebook was from another
@@ -855,8 +827,8 @@ <h2><span class="section-number">5.3.2. </span>Shap Inspection<a class="headerli
 </div></div>
  <script>
    if (window.SHAP) SHAP.ReactDom.render(
-    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {"outNames": ["f(x)"], "baseValue": 0.44600858369098717, "outValue": 0.07999999999999946, "link": "identity", "featureNames": ["Culmen Length (mm)", "Culmen Depth (mm)", "Flipper Length (mm)"], "features": {"0": {"effect": -0.4602170541487778, "value": ""}, "1": {"effect": 0.012562637521480477, "value": ""}, "2": {"effect": 0.08164583293630963, "value": ""}}, "plot_cmap": "RdBu", "labelMargin": 20}),
-    document.getElementById('iB0Q2S6VO4KTOFDZM68UF')
+    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {"outNames": ["f(x)"], "baseValue": 0.4382832618025752, "outValue": 0.16999999999999976, "link": "identity", "featureNames": ["Culmen Length (mm)", "Culmen Depth (mm)", "Flipper Length (mm)"], "features": {"0": {"effect": -0.43466120910296985, "value": ""}, "1": {"effect": 0.0357865179446255, "value": ""}, "2": {"effect": 0.13059142935576892, "value": ""}}, "plot_cmap": "RdBu", "labelMargin": 20}),
+    document.getElementById('iCFYKW8KIPBJHFNICBCAO')
   );
 </script></div></div>
 </div>
@@ -868,7 +840,7 @@ <h2><span class="section-number">5.3.2. </span>Shap Inspection<a class="headerli
 </div>
 <div class="cell_output docutils container">
 <div class="output text_html">
-<div id='iQZUNSBT8X7XX6LB56IDH'>
+<div id='iQJFX5K9AOV953NCPC924'>
 <div style='color: #900; text-align: center;'>
   <b>Visualization omitted, Javascript library not loaded!</b><br>
   Have you run `initjs()` in this notebook? If this notebook was from another
@@ -878,8 +850,8 @@ <h2><span class="section-number">5.3.2. </span>Shap Inspection<a class="headerli
 </div></div>
  <script>
    if (window.SHAP) SHAP.ReactDom.render(
-    SHAP.React.createElement(SHAP.AdditiveForceArrayVisualizer, {"outNames": ["f(x)"], "baseValue": 0.44600858369098717, "link": "identity", "featureNames": ["Culmen Length (mm)", "Culmen Depth (mm)", "Flipper Length (mm)"], "explanations": [{"outValue": 0.07999999999999946, "simIndex": 57.0, "features": {"0": {"effect": -0.4602170541487778, "value": ""}, "1": {"effect": 0.012562637521480477, "value": ""}, "2": {"effect": 0.08164583293630963, "value": ""}}}, {"outValue": 4.440892098500626e-16, "simIndex": 93.0, "features": {"0": {"effect": -0.24650734412065603, "value": ""}, "1": {"effect": -0.09102140979962124, "value": ""}, "2": {"effect": -0.1084798297707095, "value": ""}}}, {"outValue": 4.440892098500626e-16, "simIndex": 86.0, "features": {"0": {"effect": -0.22781915312457526, "value": ""}, "1": {"effect": -0.10646737671200945, "value": ""}, "2": {"effect": -0.11172205385440201, "value": ""}}}, {"outValue": 6.106226635438361e-16, "simIndex": 78.0, "features": {"0": {"effect": -0.22299031806667718, "value": ""}, "1": {"effect": -0.11959945931082784, "value": ""}, "2": {"effect": -0.10341880631348153, "value": ""}}}, {"outValue": 0.5799999999999998, "simIndex": 45.0, "features": {"0": {"effect": -0.03493518568097868, "value": ""}, "1": {"effect": 0.024424448248680557, "value": ""}, "2": {"effect": 0.14450215374131084, "value": ""}}}, {"outValue": 0.030000000000000193, "simIndex": 70.0, "features": {"0": {"effect": -0.21875982735620383, "value": ""}, "1": {"effect": -0.17593048350055923, "value": ""}, "2": {"effect": -0.021318272834223894, "value": ""}}}, {"outValue": 0.19999999999999937, "simIndex": 68.0, "features": {"0": {"effect": -0.4352333918843044, "value": ""}, "1": {"effect": 0.15218041746931135, "value": ""}, "2": {"effect": 0.037044390724005206, "value": ""}}}, {"outValue": 0.9999999999999996, "simIndex": 11.0, "features": {"0": {"effect": 0.3450061907063882, "value": ""}, "1": {"effect": 0.07888032989895469, "value": ""}, "2": {"effect": 0.13010489570366943, "value": ""}}}, {"outValue": 0.9999999999999998, "simIndex": 24.0, "features": {"0": {"effect": 0.3313608797551322, "value": ""}, "1": {"effect": 0.10823892568544116, "value": ""}, "2": {"effect": 0.1143916108684393, "value": ""}}}, {"outValue": 5.551115123125783e-17, "simIndex": 98.0, "features": {"0": {"effect": -0.26226133713640853, "value": ""}, "1": {"effect": -0.066561850317976, "value": ""}, "2": {"effect": -0.11718539623660255, "value": ""}}}, {"outValue": 0.8000000000000002, "simIndex": 42.0, "features": {"0": {"effect": 0.05272215655996993, "value": ""}, "1": {"effect": 0.1607616604085013, "value": ""}, "2": {"effect": 0.14050759934054177, "value": ""}}}, {"outValue": 0.03000000000000025, "simIndex": 69.0, "features": {"0": {"effect": -0.3124867270545742, "value": ""}, "1": {"effect": -0.11460324286700531, "value": ""}, "2": {"effect": 0.011081386230592589, "value": ""}}}, {"outValue": 0.9799999999999999, "simIndex": 20.0, "features": {"0": {"effect": 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@@ -933,7 +905,7 @@ <h2><span class="section-number">5.3.3. </span>Model Inspection<a class="headerl
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>tensor([[0.4504, 0.6628]], grad_fn=&lt;SigmoidBackward0&gt;)
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>tensor([[0.3782, 0.5659]], grad_fn=&lt;SigmoidBackward0&gt;)
 </pre></div>
 </div>
 </div>
diff --git a/searchindex.js b/searchindex.js
index 3143b7d..41fe90a 100644
--- a/searchindex.js
+++ b/searchindex.js
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