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@JesperDramsch
JesperDramsch committed Feb 11, 2024
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44 changes: 36 additions & 8 deletions _sources/notebooks/5-interpretability.ipynb
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Expand Up @@ -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)}\")"
]
},
{
Expand Down Expand Up @@ -467,7 +468,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-13T01:42:23.343022Z",
Expand All @@ -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",
Expand Down Expand Up @@ -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,
Expand All @@ -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": {},
Expand Down
122 changes: 61 additions & 61 deletions notebooks/0-basic-data-prep-and-model.html
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Expand Up @@ -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>
Expand All @@ -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>
Expand Down Expand Up @@ -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>
Expand Down Expand Up @@ -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>
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4 changes: 2 additions & 2 deletions notebooks/1-model-evaluation.html
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Expand Up @@ -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>
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