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21  <title>Lecture 12 for SOCI—269 at Amherst College</title>
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178<section id="title-slide" class="quarto-title-block center">
179  <h1 class="title"><p><span style="color:#a9a9a9;">Week 12</span><br>
180Machine Learning in<br>
181Python <svg aria-hidden="true" role="img" viewbox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#5d3c85;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"></path></svg></p></h1>
182  <p class="subtitle"><span style="color:#9c9c9c; font-family: Dynalight; font-size: 130%;">Soci—269</span></p>
183
184<div class="quarto-title-authors">
185<div class="quarto-title-author">
186<div class="quarto-title-author-name">
187Sakeef M. Karim <br><span style="font-size: 80%; color:#9c9c9c;"><em>Amherst College</em></span><br><a href="https://www.sakeefkarim.com" class="external" target="_blank"><svg aria-hidden="true" role="img" viewbox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#5d3c85;overflow:visible;position:relative;"><path d="M352 256c0 22.2-1.2 43.6-3.3 64H163.3c-2.2-20.4-3.3-41.8-3.3-64s1.2-43.6 3.3-64H348.7c2.2 20.4 3.3 41.8 3.3 64zm28.8-64H503.9c5.3 20.5 8.1 41.9 8.1 64s-2.8 43.5-8.1 64H380.8c2.1-20.6 3.2-42 3.2-64s-1.1-43.4-3.2-64zm112.6-32H376.7c-10-63.9-29.8-117.4-55.3-151.6c78.3 20.7 142 77.5 171.9 151.6zm-149.1 0H167.7c6.1-36.4 15.5-68.6 27-94.7c10.5-23.6 22.2-40.7 33.5-51.5C239.4 3.2 248.7 0 256 0s16.6 3.2 27.8 13.8c11.3 10.8 23 27.9 33.5 51.5c11.6 26 20.9 58.2 27 94.7zm-209 0H18.6C48.6 85.9 112.2 29.1 190.6 8.4C165.1 42.6 145.3 96.1 135.3 160zM8.1 192H131.2c-2.1 20.6-3.2 42-3.2 64s1.1 43.4 3.2 64H8.1C2.8 299.5 0 278.1 0 256s2.8-43.5 8.1-64zM194.7 446.6c-11.6-26-20.9-58.2-27-94.6H344.3c-6.1 36.4-15.5 68.6-27 94.6c-10.5 23.6-22.2 40.7-33.5 51.5C272.6 508.8 263.3 512 256 512s-16.6-3.2-27.8-13.8c-11.3-10.8-23-27.9-33.5-51.5zM135.3 352c10 63.9 29.8 117.4 55.3 151.6C112.2 482.9 48.6 426.1 18.6 352H135.3zm358.1 0c-30 74.1-93.6 130.9-171.9 151.6c25.5-34.2 45.2-87.7 55.3-151.6H493.4z"></path></svg></a> <a href="mailto:[email protected]"><svg aria-hidden="true" role="img" viewbox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#5d3c85;overflow:visible;position:relative;"><path d="M48 64C21.5 64 0 85.5 0 112c0 15.1 7.1 29.3 19.2 38.4L236.8 313.6c11.4 8.5 27 8.5 38.4 0L492.8 150.4c12.1-9.1 19.2-23.3 19.2-38.4c0-26.5-21.5-48-48-48H48zM0 176V384c0 35.3 28.7 64 64 64H448c35.3 0 64-28.7 64-64V176L294.4 339.2c-22.8 17.1-54 17.1-76.8 0L0 176z"></path></svg></a> <br> <span style="font-weight: 300; font-size: 60%; color: #9c9c9c;">AN INTRODUCTION TO QUANTITATIVE SOCIOLOGY—CULTURE &amp; POWER</span> 
188</div>
189</div>
190</div>
191
192</section>
193<section>
194<section id="a-very-gentle-introduction-november-17th" class="title-slide slide level1 center" data-background-color="#43464B">
195<h1>A <span style="text-decoration: underline;"><em>Very</em></span> Gentle Introduction–<br>November 17<span style="font-size: 70%;"><sup>th</sup></span></h1>
196
197</section>
198<section id="reminders" class="slide level2">
199<h2>Reminders</h2>
200<div class="centre-text">
201<p>You have a <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>bit</em></strong></span> more time with<br>the second coding assignment.</p>
202</div>
203</section>
204<section id="reminders-1" class="slide level2">
205<h2>Reminders</h2>
206<div style="margin-top: 90px; text-align: center;">
207<div class="callout callout-warning callout-titled callout-style-default">
208<div class="callout-body">
209<div class="callout-title">
210<div class="callout-icon-container">
211<i class="callout-icon"></i>
212</div>
213<p><strong>Second Coding Assignment <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>Soft</em></strong></span> Deadline</strong></p>
214</div>
215<div class="callout-content">
216<p>Your second coding assignment is due by <strong>8:00 PM</strong>
216 on Friday, November 21<span style="font-size:70%"><sup>st</sup></span>.<br>This is a <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>soft</em></strong></span> deadline.</p>
217</div>
218</div>
219</div>
220</div>
221</section>
222<section id="reminders-2" class="slide level2">
223<h2>Reminders</h2>
224<div style="margin-top: 90px; text-align: center;">
225<div class="callout callout-important callout-titled callout-style-default">
226<div class="callout-body">
227<div class="callout-title">
228<div class="callout-icon-container">
229<i class="callout-icon"></i>
230</div>
231<p><strong>Second Coding Assignment <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>Hard</em></strong></span> Deadline</strong></p>
232</div>
233<div class="callout-content">
234<p>All coding assignments (in <code>Python</code> <svg aria-hidden="true" role="img" viewbox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#5d3c85;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"></path></svg>) <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>must</em></strong></span> be submitted by <strong>8:00 PM</strong> on Wednesday, December 3<span style="font-size:70%"><sup>rd</sup></span>.</p>
235</div>
236</div>
237</div>
238</div>
239</section>
240<section id="reminders-3" class="slide level2">
241<h2>Reminders</h2>
242<div style="text-align: center; margin-top: 25px;">
243<p>Once again, assignment instructions are available online.</p>
244<p align="center">
245<iframe class="iframe" src="https://soci-269-a2.netlify.app/" height="450" width="1040" style="border: 1px solid #2e3846;">
246</iframe>
247</p>
248<div style="text-align: center; font-size: 75%; margin-top:-40px">
249<p><a href="https://soci-269-a1.netlify.app/" data-preview-link="true">Full Page</a></p>
250</div>
251</div>
252</section>
253<section id="an-update" class="slide level2">
254<h2>An Update</h2>
255<h4 id="final-presentation-and-term-paper"><span style="color:#a9a9a9;">Final Presentation and Term Paper</span></h4>
256<div style="text-align: center; font-size: 87%; margin-top: 20px; margin-bottom: 20px;">
257<p>Guidelines for your <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>final presentation</em></strong></span> and <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>term paper</em></strong></span> are live.</p>
258<p align="center">
259<iframe class="iframe" src="https://soci-269-final.netlify.app/" height="450" width="1040" style="border: 1px solid #2e3846;">
260</iframe>
261</p>
262<div style="text-align: center; font-size: 60%; margin-top:-40px">
263<p><a href="https://soci-269-final.netlify.app/" data-preview-link="true">Full Page</a></p>
264</div>
265</div>
266</section></section>
267<section>
268<section id="what-is-machine-learning" class="title-slide slide level1 center" data-background-color="#43464B">
269<h1>What is Machine Learning?</h1>
270
271</section>
272<section id="some-concrete-definitions" class="slide level2">
273<h2>Some Concrete Definitions</h2>
274<div style="margin-top: 50px; font-size: 95%;">
275<div class="fragment">
276<p>Grimmer and colleagues <span class="citation" data-cites="grimmer2021">(<a href="#/references" role="doc-biblioref" onclick="">2021:396</a>
276, <span style="font-size: 55%; font-family: Red Hat Display; color: white; background-color: #5d3c85"><strong>EMPHASIS ADDED</strong></span>)</span></p>
277<div class="shrinker-plus">
278<blockquote>
279<p>A class of flexible algorithmic and statistical techniques for <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>prediction</strong></span> and <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>dimension reduction</strong></span>.</p>
280</blockquote>
281</div>
282</div>
283<div style="margin-top: 40px;">
284
285</div>
286<div class="fragment">
287<p>Molina and Garip <span class="citation" data-cites="molina_machine_2019">(<a href="#/references" role="doc-biblioref" onclick="">2019:28</a>, <span style="font-size: 55%; font-family: Red Hat Display; color: white; background-color: #5d3c85"><strong>EMPHASIS ADDED</strong></span>)</span></p>
288<div class="shrinker-plus">
289<blockquote>
290<p>A way to learn from data and estimate complex functions that <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>discover representations of some input</strong> (<span class="math inline">X</span>)</span>, or <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>link the input to an output</strong> (<span class="math inline">Y</span>)</span> in order to <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>make predictions on new data</strong></span>.</p>
291</blockquote>
292</div>
293</div>
294</div>
295</section>
296<section id="supervised-machine-learning-sml" class="slide level2 smaller">
297<h2>Supervised Machine Learning <span style="font-size: 70%;">(SML)</span></h2>
298<h3 id="high-level-overview"><span style="color:#9c9c9c; font-size: 85%;">High-Level Overview</span></h3>
299<div style="margin-top: 40px;">
300
301</div>
302<ul>
303<li class="fragment"><p>Once deployed, SML algorithms learn the complex patterns linking <span class="math inline">X</span>—a set of <span style="text-decoration: underline; text-decoration-color: #5d3c85;"><em>features</em></span> (or independent variables)—to a <span style="text-decoration: underline; text-decoration-color: #5d3c85;"><em>target variable</em></span> (or outcome), <span class="math inline">Y</span>.</p>
304<ul>
305<li class="fragment"><p><span style="font-size: 85%;">The goal of SML is to <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>optimize predictions</strong></span>—i.e., to find functions or algorithms that offer substantial predictive power when confronted with <span style="text-decoration: underline; text-decoration-color: #5d3c85;"><em>new or unseen data</em></span>.</span></p></li>
306<li class="fragment"><p><span style="font-size: 85%;">Examples of SML algorithms include <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>logistic regressions</strong></span>, <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>random forests</strong></span>, <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>ridge regressions</strong></span>, <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>support vector machines</strong></span> and <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>neural networks</strong></span>.</span></p></li>
307</ul></li>
308<li class="fragment"><p>A quick note on terminology</p>
309<ul>
310<li class="fragment"><p><span style="font-size: 85%;">If a target variable is <span style="text-decoration: underline; text-decoration-color: #5d3c85;"><em>quantitative</em></span>, we are dealing with a <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>regression</strong></span> problem.</span></p></li>
311<li class="fragment"><p><span style="font-size: 85%;">If a target variable is <span style="text-decoration: underline; text-decoration-color: #5d3c85;"><em>qualitative</em></span>, we are dealing with a <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>classification</strong></span> problem.</span></p></li>
312</ul></li>
313</ul>
314</section>
315<section id="supervised-machine-learning-sml-cont." class="slide level2 smaller">
316<h2>Supervised Machine Learning <span style="font-size: 70%;">(SML)</span> <em>cont</em>.</h2>
317<h3 id="an-illustration"><span style="color:#9c9c9c; font-size: 85%;">An Illustration</span></h3>
318<div class="panel-tabset">
319<ul id="tabset-1" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-1-1">(1) Feed Input Data</a></li><li><a href="#tabset-1-2">(2) Develop Algorithm</a></li><li><a href="#tabset-1-3">(3) Predictions on New Data</a></li></ul>
320<div class="tab-content">
321<div id="tabset-1-1">
322<div style="margin-top: 20px;">
323
324</div>
325<div class="cell styled-output" data-layout-align="center">
326<div class="cell-output cell-output-stdout">
327<pre><code># A tibble: 80,000 × 6
328   fruit      shape       weight_g colour texture origin    
329   &lt;chr&gt;      &lt;chr&gt;          &lt;dbl&gt; &lt;chr&gt;  &lt;chr&gt;   &lt;chr&gt;     
330 1 apple      spherical        150 red    crispy  china     
331 2 banana     curved           120 yellow creamy  ecuador   
332 3 orange     spherical        148 orange juicy   egypt     
333 4 watermelon spherical       4500 green  juicy   spain     
334 5 strawberry conical           13 red    juicy   mexico    
335 6 grape      spherical          5 green  juicy   chile     
336 7 mango      ellipsoidal      240 yellow juicy   india     
337 8 pineapple  conical         2100 yellow juicy   costa rica
338 9 apple      spherical        140 green  crispy  usa       
33910 banana     curved           110 yellow creamy  ecuador   
340# ℹ 79,990 more rows</code></pre>
341</div>
342</div>
343</div>
344<div id="tabset-1-2">
345<div style="text-align: center; margin-top: 10px;">
346<div class="quarto-figure quarto-figure-center">
347<figure>
348<p><a href="./media/neural_network.png" class="lightbox" data-gallery="quarto-lightbox-gallery-1" title="via scikit-learn"><img data-src="./media/neural_network.png" style="width:32.0%" alt="via scikit-learn"></a></p>
349<figcaption><a href="https://scikit-learn.org/stable/modules/neural_networks_supervised.html" class="external" target="_blank">via <span style="font-size: 85%;"><code>scikit-learn</code></span></a></figcaption>
350</figure>
351</div>
352</div>
353</div>
354<div id="tabset-1-3">
355<div class="columns">
356<div class="column" style="width:55%;">
357<div class="fragment">
358<div class="cell styled-output">
359<div class="cell-output cell-output-stdout">
360<pre><code># A tibble: 1 × 6
361  fruit shape   weight_g colour texture origin
362  &lt;chr&gt; &lt;chr&gt;      &lt;int&gt; &lt;chr&gt;  &lt;chr&gt;   &lt;chr&gt; 
3631 &lt;NA&gt;  conical        6 red    juicy   china </code></pre>
364</div>
365</div>
366</div>
367<div class="fragment">
368<div class="cell styled-output">
369<div class="cell-output cell-output-stdout">
370<pre><code># A tibble: 8 × 2
371  fruit      probability
372  &lt;chr&gt;            &lt;dbl&gt;
3731 apple             0.03
3742 banana            0   
3753 orange            0.05
3764 watermelon        0   
3775 strawberry        0.71
3786 grape             0.21
3797 mango             0   
3808 pineapple         0   </code></pre>
381</div>
382</div>
383</div>
384</div><div class="column" style="width:45%;">
385<div class="fragment">
386<div class="cell">
387<div class="cell-output cell-output-stdout">
388<pre><code># A tibble: 1 × 1
389  prediction
390  &lt;chr&gt;     
3911 strawberry</code></pre>
392</div>
393</div>
394<p><img data-src="./media/strawberry.png" style="width:40.0%"></p>
395<div class="attribution">
396<p><a href="https://www.flaticon.com/free-icons/strawberry" title="strawberry icons" style="font-size: 50%;">Strawberry icons created by BZZRINCANTATION - Flaticon</a></p>
397</div>
398</div>
399</div></div>
400</div>
401</div>
402</div>
403</section>
404<section id="unsupervised-machine-learning-uml" class="slide level2 smaller">
405<h2>Unsupervised Machine Learning <span style="font-size: 70%;">(UML)</span></h2>
406<h3 id="high-level-overview-1"><span style="color:#9c9c9c; font-size: 85%;">High-Level Overview</span></h3>
407<ul>
408<li class="fragment"><p>UML techniques search for a representation of the inputs (or features) that is more useful than <span class="math inline">X</span> itself <span class="citation" data-cites="molina_machine_2019">(<a href="#/references" role="doc-biblioref" onclick="">
408Molina and Garip 2019</a>)</span>.</p>
409<ul>
410<li class="fragment"><span style="font-size: 95%;">Put another way, UML algorithms search for <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>hidden structure or latent patterns in high dimensional space</strong></span>.</span></li>
411</ul></li>
412<li class="fragment"><p>In UML, there is no <span style="text-decoration: underline; text-decoration-color: #5d3c85;"><em>observed</em></span> outcome variable <span class="math inline">Y</span>—or target—to <em>supervise</em> the estimation process. Instead, we only have a vector of inputs to work with.</p></li>
413<li class="fragment"><p>The goal in UML is to develop a lower-dimensional representation of complex data by inductively learning from the <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>interrelationships among inputs</em></strong></span>.</p>
414<ul>
415<li class="fragment"><span style="font-size: 85%;">This can be achieved by reducing a vector of features to a smaller set of scales—e.g., via <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>principal component analysis</strong></span>—or partitioning the sample into a small number of unobserved groups (e.g., via <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>k</em>-means clustering</strong></span>).</span></li>
416</ul></li>
417</ul>
418</section>
419<section id="unsupervised-machine-learning-uml-cont." class="slide level2">
420<h2>Unsupervised Machine Learning <span style="font-size: 70%;">(UML)</span> <em>cont</em>.</h2>
421<h3 id="an-illustration-1"><span style="color:#9c9c9c; font-size: 85%;">An Illustration</span></h3>
422<div class="cell">
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884<tr class="gt_col_headings header">
885<th id="V-Party-Name" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" style="border-top-width: 0px; border-top-style: solid; border-top-color: black; font-family: Chivo; font-size: 14px; vertical-align: bottom; font-weight: 200; text-transform: uppercase" scope="col">V-Party Name</th>
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887<th id="Underlying-Question" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" style="border-top-width: 0px; border-top-style: solid; border-top-color: black; font-family: Chivo; font-size: 14px; vertical-align: bottom; font-weight: 200; text-transform: uppercase" scope="col">Underlying Question</th>
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891<tr class="odd">
892<td class="gt_row gt_center" headers="V-Party Name"><code>v2paanteli</code></td>
893<td class="gt_row gt_center" headers="Label">Anti-Elitism</td>
894<td class="gt_row gt_left" headers="Underlying Question">How important is anti-elite rhetoric for this party?</td>
895</tr>
896<tr class="even">
897<td class="gt_row gt_center" headers="V-Party Name"><code>v2papeople</code></td>
898<td class="gt_row gt_center" headers="Label">People-Centrism</td>
899<td class="gt_row gt_left" headers="Underlying Question">Do leaders of this party glorify the ordinary people and identify themselves as part of them?</td>
900</tr>
901<tr class="odd">
902<td class="gt_row gt_center" headers="V-Party Name"><code>v2paculsup</code></td>
903<td class="gt_row gt_center" headers="Label">Cultural Chauvinism</td>
904<td class="gt_row gt_left" headers="Underlying Question">To what extent does the party leadership promote the cultural superiority of a specific social group or the nation as a whole?</td>
905</tr>
906<tr class="even">
907<td class="gt_row gt_center" headers="V-Party Name"><code>v2paminor</code></td>
908<td class="gt_row gt_center" headers="Label">Minority Rights</td>
909<td class="gt_row gt_left" headers="Underlying Question">According to the leadership of this party, how often should the will of the majority be implemented even if doing so w
909ould violate the rights of minorities?</td>
910</tr>
911<tr class="odd">
912<td class="gt_row gt_center" headers="V-Party Name"><code>v2paplur</code></td>
913<td class="gt_row gt_center" headers="Label">Political Pluralism</td>
914<td class="gt_row gt_left" headers="Underlying Question">Prior to this election, to what extent was the leadership of this political party clearly committed to free and fair elections with multiple parties, freedom of speech, media, assembly and association?</td>
915</tr>
916<tr class="even">
917<td class="gt_row gt_center" headers="V-Party Name"><code>v2paopresp</code></td>
918<td class="gt_row gt_center" headers="Label">Demonization of Opponents</td>
919<td class="gt_row gt_left" headers="Underlying Question">Prior to this election, have leaders of this party used severe personal attacks or tactics of demonization against their opponents?</td>
920</tr>
921<tr class="odd">
922<td class="gt_row gt_center" headers="V-Party Name"><code>v2paviol</code></td>
923<td class="gt_row gt_center" headers="Label">Rejection of Political Violence</td>
924<td class="gt_row gt_left" headers="Underlying Question">To what extent does the leadership of this party explicitly discourage the use of violence against domestic political opponents?</td>
925</tr>
926</tbody>
927</table>
928
929</div>
930</div>
931</div>
932</section>
933<section id="unsupervised-machine-learning-uml-cont.-1" class="slide level2 smaller">
934<h2>Unsupervised Machine Learning <span style="font-size: 70%;">(UML)</span> <em>cont</em>.</h2>
935<h3 id="an-illustration-2"><span style="color:#9c9c9c; font-size: 85%;">An Illustration</span></h3>
936
937<img data-src="./media/pca_shift.gif" class="r-stretch quarto-figure-center"><p class="caption"><span style="font-size: 65%; text-align: center; margin-top: -10px">Karim and Lukk’s <em>The Radicalization of Mainstream Parties in the 21<span style="font-size: 65%;"><sup>st</sup></span> Century</em></span></p></section></section>
938<section>
939<section id="machine-learning-vs-classical-statistics" class="title-slide slide level1 center" data-background-color="#43464B">
940<h1>Machine Learning vs Classical Statistics</h1>
941
942</section>
943<section id="the-two-cultures" class="slide level2">
944<h2>The Two Cultures</h2>
945<div style="margin-top: 20px; font-size: 85%;">
946<ul>
947<li class="fragment"><p>As Grimmer and colleagues <span class="citation" data-cites="grimmer2021">(<a href="#/references" role="doc-biblioref" onclick="">2021</a>)</span> note, “machine learning is as much a <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>culture</strong></span> defined by a distinct set of values and tools as it is a set of algorithms.”</p></li>
948<li class="fragment"><p>This point has, of course, been made elsewhere.</p>
949<ul>
950<li class="fragment"><p><span style="font-size: 95%;">Leo Breiman <span class="citation" data-cites="breiman2001">(<a href="#/references" role="doc-biblioref" onclick="">2001</a>)</span> famously used the imagery of warring <em>cultures</em> to describe two major traditions—(i) the <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>generative modelling culture</strong></span> and (ii) the <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>predictive modelling culture</strong></span>—that have achieved hegemony within the world of statistical modelling.</span></p></li>
951<li class="fragment"><p><span style="font-size: 95%;">The terms <span style="text-decoration: underline; text-decoration-color: #5d3c85;"><em>generative</em></span> and <span style="text-decoration: underline; text-decoration-color: #5d3c85;"><em>predictive</em></span> (as opposed to <em>data</em> and <em>algorithmic</em>) come from David Donoho’s <span class="citation" data-cites="donoho2017">(<a href="#/references" role="doc-biblioref" onclick="">2017</a>)</span> <em>50 Years of Data Science</em>.</span></p></li>
952</ul></li>
953</ul>
954</div>
955</section>
956<section id="the-two-cultures-cont." class="slide level2 no-hover">
957<h2>The Two Cultures <em>cont</em>.</h2>
958<p><br></p>
959<div class="cell">
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1374#ofqqljgkfa .gt_footnote_marks {
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1380#ofqqljgkfa .gt_asterisk {
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1410#ofqqljgkfa div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
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1413
1414tbody tr:last-child {
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1419<table class="gt_table caption-top" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
1420<thead>
1421<tr class="gt_col_headings header">
1422<th id="Quantity-of-Interest" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" style="border-top-width: 0px; border-top-style: solid; border-top-color: black; font-family: Chivo; font-size: 14px; vertical-align: bottom; font-weight: 200; text-transform: uppercase" scope="col">Quantity of Interest</th>
1423<th id="Primary-Goals" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" style="border-top-width: 0px; border-top-style: solid; border-top-color: black; font-family: Chivo; font-size: 14px; vertical-align: bottom; font-weight: 200; text-transform: uppercase" scope="col">Primary Goals</th>
1424<th id="Key-Strengths" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" style="border-top-width: 0px; border-top-style: solid; border-top-color: black; font-family: Chivo; font-size: 14px; vertical-align: bottom; font-weight: 200; text-transform: uppercase" scope="col">Key Strengths</th>
1425<th id="Key-Limitations" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" style="border-top-width: 0px; border-top-style: solid; border-top-color: black; font-family: Chivo; font-size: 14px; vertical-align: bottom; font-weight: 200; text-transform: uppercase" scope="col">Key Limitations</th>
1426</tr>
1427</thead>
1428<tbody class="gt_table_body">
1429<tr class="gt_group_heading_row odd">
1430<td colspan="4" id="Generative (i.e., Classical Statistics)" class="gt_group_heading" data-quarto-table-cell-role="th" style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: black" scope="colgroup">Generative (i.e., Classical Statistics)</td>
1431</tr>
1432<tr class="gt_row_group_first even">
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d3L3rxMd2999yb6qQXPKWmAkXVNxjU/pI/+7Pus5//bLdoZpGvyoWba5qKnHY2uS2E7I0Zru0TA8ciUWynzdW3FdXfrjGrwad3ZnQsA2NU89CWxi6O1CQoLbtxP0Dyf+tb3+qe/7zndXfffY8MWgalxUhjy7IqhbqXnnhSd8H
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" style="height:45px;"></td>
1434<td class="gt_row gt_left" headers="Generative (i.e., Classical Statistics)  Primary Goals">Inferring relationships between <strong><em>X</em></strong> and <strong><em>Y</em></strong></td>
1435<td class="gt_row gt_left" headers="Generative (i.e., Classical Statistics)  Key Strengths">Interpretability; emphasis on uncertainty around estimates; explanatory power</td>
1436<td class="gt_row gt_left" headers="Generative (i.e., Classical Statistics)  Key Limitations">Bounded by statistical assumptions, inattention to variance across samples</td>
1437</tr>
1438<tr class="gt_group_heading_row odd">
1439<td colspan="4" id="Predictive (i.e., Machine Learning)" class="gt_group_heading" data-quarto-table-cell-role="th" style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: black" scope="colgroup">Predictive (i.e., Machine Learning)</td>
1440</tr>
1441<tr class="gt_row_group_first even">
1442<td class="gt_row gt_center" headers="Predictive (i.e., Machine Learning)  Quantity of Interest"><img data-src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJoAAADgCAYAAAD7Rn+wAAAMP2lDQ1BJQ0MgUHJvZmlsZQAASImVVwdYU8kWnluSkEBoAQSkhN4EkRpASggt9N5EJSQBQokxEFTs6KKCaxcL2NBVEQUrzYIidhbF3hcLKsq6WLArb1JA133le/N9c+e//5z5z5lz5965A4DacY5IlIeqA5AvLBTHBvvTk1NS6aSnAAcUQAVeQJnDLRAxo6PDASxD7d/Lu+sAkbZX7KVa/+z/r0WDxy/gAoBEQ5zBK+DmQ3wQALyKKxIXAkCU8mZTCkVSDCvQEsMAIV4oxVlyXCXFGXK8V2YTH8uCuB0AJRUOR5wFgOolyNOLuFlQQ7UfYkchTyAEQ
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" style="height:45px;"></td>
1443<td class="gt_row gt_left" headers="Predictive (i.e., Machine Learning)  Primary Goals">Generating accurate predictions of <strong><em>Y</em></strong></td>
1444<td class="gt_row gt_left" headers="Predictive (i.e., Machine Learning)  Key Strengths">Predictive power; potential to simplify high dimensional data; relatively unconstrained by statistical assumptions</td>
1445<td class="gt_row gt_left" headers="Predictive (i.e., Machine Learning)  Key Limitations">Inattention to explanatory processes, opaque links between <strong><em>X</em></strong> and <strong><em>Y</em></strong></td>
1446</tr>
1447</tbody>
1448</table>
1449
1450</div>
1451</div>
1452</div>
1453<div class="scrollable-note">
1454<p><span style="font-size: 80%;"><strong>Note</strong>: To be sure, the putative <em>strengths</em> and <em>weaknesses</em> of these modelling “cultures” have been hotly debated.</span></p>
1455</div>
1456</section>
1457<section id="the-affordances-of-machine-learning" class="slide level2 smaller">
1458<h2>The Affordances of Machine Learning</h2>
1459<div style="margin-top: 25px;">
1460
1461</div>
1462<ul>
1463<li class="fragment"><p>Advances in machine learning can provide empirical leverage to social scientists and <em>sharpen</em> social theory in one fell swoop.</p></li>
1464<li class="fragment"><p>Lundberg, Brand and Jeon <span class="citation" data-cites="lundberg_researcher_2022">(<a href="#/references" role="doc-biblioref" onclick="">2022</a>)</span>, for instance, argue that adopting a machine learning framework can help social scientists:</p>
1465<ul>
1466<li class="fragment"><span style="font-size: 85%;">Amplify <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>human coding</strong></span></span></li>
1467<li class="fragment"><span style="font-size: 85%;">Summarize <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>complex data structures</strong></span></span></li>
1468<li class="fragment"><span style="font-size: 85%;">Relax <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>statistical assumptions</strong></span></span></li>
1469<li class="fragment"><span style="font-size: 85%;">Target <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>researcher attention</strong></span></span></li>
1470</ul></li>
1471<li class="fragment"><p>While ML is often associated with <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>induction</strong></span>, van Loon <span class="citation" data-cites="loon_machine_2022">(<a href="#/references" role="doc-biblioref" onclick="">2022</a>)</span> argues that SML algorithms can help us deductively resolve <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>
1471predictability hypotheses</strong></span> as well.</p>
1472<ul>
1473<li class="fragment"><span style="font-size: 85%;"><em>What plays a larger role in shaping human behaviour—nature or nurture</em>?</span></li>
1474</ul></li>
1475</ul>
1476</section></section>
1477<section>
1478<section id="key-terms-and-concepts-in-the-sml-setting" class="title-slide slide level1 center" data-background-color="#43464B">
1479<h1>Key Terms and Concepts in the SML Setting</h1>
1480
1481</section>
1482<section id="bias-variance-tradeoff" class="slide level2 smaller">
1483<h2>Bias-Variance Tradeoff</h2>
1484<div style="margin-top: 40px;">
1485
1486</div>
1487<div class="columns">
1488<div class="column" style="width:62%;">
1489<p><a href="./media/bias_variance.jpeg" class="lightbox" data-gallery="quarto-lightbox-gallery-2"><img data-src="./media/bias_variance.jpeg"></a></p>
1490<div style="text-align: center;">
1491<p><span style="font-size: 60%; text-align: center;">Image can be retrieved <a href="https://i.imgur.com/qRvK70r.jpg" class="external" target="_blank">here</a>.</span></p>
1492</div>
1493</div><div class="column" style="width:38%;">
1494<ul>
1495<li class="fragment"><p><span style="font-size: 75%;"><span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>Bias</strong></span> emerges when we build SML algorithms that fail to sufficiently map the patterns—or pick up the empirical signal–linking <strong><span class="math inline">X</span></strong> and <strong><span class="math inline">Y</span></strong>. Think: <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>underfitting</strong></span>.</span></p></li>
1496<li class="fragment"><p><span style="font-size: 75%;"><span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>Variance</strong></span> arises when our algorithms not only pick up the signal linking <strong><span class="math inline">X</span></strong> and <strong><span class="math inline">Y</span></strong>, but some of the noise in our data as well. Think: <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>overfitting</strong></span>.</span></p></li>
1497<li class="fragment"><p><span style="font-size: 75%;">When adopting an SML framework, researchers try to <span style="text-decoration: underline; text-decoration-color: #5d3c85;"><em>strike the optimal balance</em></span> between bias and variance.</span></p></li>
1498</ul>
1499</div></div>
1500</section>
1501<section id="training-validation-and-testing" class="slide level2 smaller">
1502<h2>Training, Validation and Testing</h2>
1503<div style="margin-top: 30px;">
1504
1505</div>
1506<div class="fragment">
1507<ul>
1508<li>In an SML setting, we want to reduce our algorithm’s <span style="text-decoration: underline; text-decoration-color: #5d3c85;"><em>generalization</em></span> or <span style="text-decoration: underline; text-decoration-color: #5d3c85;"><em>test error</em></span>—i.e., “the prediction error of a model on new data” <span class="citation" data-cites="molina_machine_2019">(<a href="#/references" role="doc-biblioref" onclick="">Molina and Garip 2019</a>)</span>.</li>
1509</ul>
1510</div>
1511<div class="fragment">
1512<ul>
1513<li>To arrive at an estimate of our model’s performance, we can (randomly) partition our global sample of observations into disjoint <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>sets</strong></span> or subsamples.</li>
1514</ul>
1515</div>
1516<div class="fragment">
1517<div class="panel-tabset">
1518<ul id="tabset-2" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-2-1">Training</a></li><li><a href="#tabset-2-2">Validation</a></li><li><a href="#tabset-2-3">Testing</a></li></ul>
1519<div class="tab-content">
1520<div id="tabset-2-1">
1521<ul>
1522<li class="fragment"><p><span style="font-size: 85%;">We can use a <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>training set</strong></span> to fit our algorithm—to find weights (or coefficients), recursively split the feature space to grow decision trees and s
1522o on.</span></p></li>
1523<li class="fragment"><p><span style="font-size: 85%;">Training data should constitute the <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>largest</strong></span> of our three disjoint sets.</span></p></li>
1524</ul>
1525</div>
1526<div id="tabset-2-2">
1527<ul>
1528<li class="fragment"><p><span style="font-size: 85%;">We can use a <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>validation set</strong></span> to find the right estimator out of a series of candidate algorithms—or select the best-fitting parameterization of a single algorithm.</span></p></li>
1529<li class="fragment"><p><span style="font-size: 85%;">Often, using both training <em>and</em> validation sets can be costly: data sparsity can amplify <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>variance</strong></span> across samples or datasets.</span></p></li>
1530<li class="fragment"><p><span style="font-size: 85%;">Thus, when limited to smaller samples, analysts often <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>combine</strong></span> training and validation—say, by recycling training data for model tuning and selection.</span></p></li>
1531</ul>
1532</div>
1533<div id="tabset-2-3">
1534<ul>
1535<li class="fragment"><p><span style="font-size: 85%;">We can use a <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>testing set</strong></span> to generate a measure of our model’s predictive accuracy (e.g., the <code>F1 score</code> for classification problems)—or to derive our generalization error.</span></p></li>
1536<li class="fragment"><p><span style="font-size: 85%;">This subsample is used only <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>once</strong></span> (to report the performance metric); put another way, it <strong>cannot</strong> be used to train, tune or select our algorithm.</span></p></li>
1537</ul>
1538</div>
1539</div>
1540</div>
1541</div>
1542</section>
1543<section id="k-fold-cross-validation" class="slide level2 smaller">
1544<h2><span class="math inline">k</span>-Fold Cross-Validation</h2>
1545<div class="panel-tabset">
1546<ul id="tabset-3" class="panel-tabset-tabby"><li><a data-tabby-default="" href="#tabset-3-1">High-Level Overview</a></li><li><a href="#tabset-3-2">An Illustration</a></li></ul>
1547<div class="tab-content">
1548<div id="tabset-3-1">
1549<ul>
1550<li class="fragment"><p>Unlike conventional approaches to sample partition, <span style="color: #5d3c85;"><strong><span class="math inline">k</span></strong></span> or <span style="color: #5d3c85;"><strong><span class="math inline">v</span>-fold cross-validation</strong></span> allows us to learn from <em>all</em> our data.</p></li>
1551<li class="fragment"><p><span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><span class="math inline">k</span>-fold cross-validation</strong></span> proceeds as follows:</p>
1552<ul>
1553<li class="fragment"><span style="font-size: 75%;">We randomly divide our overall sample into <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><span class="math inline">k</span> subsets or folds</strong></span>.</span></li>
1554<li class="fragment"><span style="font-size: 75%;">We train our algorithm on <span class="math inline">k - 1</span> folds, holding just <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>one group</strong></span> out for model assessment.</span></li>
1555<li class="fragment"><span style="font-size: 75%;">We <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>repeat</strong></span> this process <span class="math inline">k</span> times—every fold is held out once and used to fit the model <span class="math inline">k - 1</span> times.</span></li>
1556<li class="fragment"><span style="font-size: 75%;">We then pool or average the evaluation metrics (e.g., predictive accuracy) for <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>all</strong></span> the held-out runs.</span></li>
1557</ul></li>
1558<li class="fragment"><p><span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>Stratified</strong></span> <span class="math inline">k</span>-fold cross-validation ensures that the distribution of class labels (or for numeric targets, the mean) is relatively constant across folds.</p></li>
1559</ul>
1560</div>
1561<div id="tabset-3-2">
1562<div style="text-align: center;">
1563<p><span style="font-size: 80%; text-align: center;">Stylized example of <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><em>five-fold</em></span> cross-validation</span></p>
1564</div>
1565<div class="cell" data-layout-align="center">
1566<div class="cell-output-display">
1567<div class="quarto-figure quarto-figure-center">
1568<figure>
1569<p><a href="index_files/figure-revealjs/unnamed-chunk-9-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-3"><img data-src="index_files/figure-revealjs/unnamed-chunk-9-1.png" class="quarto-figure quarto-figure-center" style="width:150.0%"></a></p>
1570</figure>
1571</div>
1572</div>
1573</div>
1574</div>
1575</div>
1576</div>
1577<!-- ![](./images/ca.gif){.absolute top="147" left="150" width="750" height="485"} -->
1578</section>
1579<section id="hyperparameter-optimization" class="slide level2 shrinker">
1580<h2>Hyperparameter Optimization</h2>
1581<div style="margin-top: 30px;">
1582
1583</div>
1584<ul>
1585<li class="fragment"><p>In SML settings, we automatically learn the <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>
1585parameters</strong></span> (e.g., coefficients) of our algorithms during estimation.</p></li>
1586<li class="fragment"><p><span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>Hyperparameters</strong></span>, on the other hand, are <em>chosen</em> by the analyst, guide the entire learning or estimation process, and can powerfully shape our algorithm’s predictive performance.</p>
1587<ul>
1588<li class="fragment"><span style="font-size: 75%;">Examples of hyperparameters include the <span style="color: #5d3c85;"><strong><span class="math inline">k</span></strong></span> in nearest neighbours models, the <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><span class="math inline">\alpha</span> penalty term</strong></span> in ridge regressions, or the <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>number of hidden layers</strong></span> in a neural network.</span></li>
1589</ul></li>
1590<li class="fragment"><p>How can analysts settle on the right hyperparameter value(s)?</p>
1591<ul>
1592<li class="fragment"><span style="font-size: 75%;">Test different values via trial and error.</span></li>
1593<li class="fragment"><span style="font-size: 90%;">Use automated procedures like&nbsp; <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html" class="external" target="_blank"><span style="font-size: 90%;"><code>GridSearchCV</code></span></a>&nbsp; from <span style="font-size: 90%;"><code>scikit-learn</code></span>.</span></li>
1594</ul></li>
1595</ul>
1596</section></section>
1597<section>
1598<section id="k-nearest-neighbours" class="title-slide slide level1 center" data-background-color="#43464B">
1599<h1><span class="math inline">k</span>-Nearest Neighbours</h1>
1600
1601</section>
1602<section id="brief-overview-of-knn" class="slide level2 smaller">
1603<h2>Brief Overview of KNN</h2>
1604<div style="margin-top: 40px;">
1605
1606</div>
1607<ul>
1608<li class="fragment"><p><span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><span class="math inline">k</span>-nearest neighbours</strong></span> (KNNs) are simple, non-parametric algorithms that predict values of <span class="math inline">Y</span> based on the distance between rows (or observations’ inputs).</p></li>
1609<li class="fragment"><p>The estimation of KNNs proceeds as follows:</p>
1610<ul>
1611<li class="fragment"><span style="font-size: 85%;">The analyst defines the <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>distance metric</strong></span> (e.g., Euclidean, Manhattan) they will use to determine how similar any two observations are based on their <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>vector of features</strong></span>.</span></li>
1612<li class="fragment"><span style="font-size: 80%;">The analyst defines a value for the <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>hyperparameter <span class="math inline">k</span></strong></span>—that is, the number of nearest neighbours to find in the training subsample.</span><br>
1613</li>
1614<li class="fragment"><span style="font-size: 80%;">When fed a <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>new data point</strong></span>, KNNs find the <span class="math inline">k</span> nearest neighbours in the training data, and:</span>
1615<ul>
1616<li class="fragment"><span style="font-size: 80%;">Assign the new observation to the class that most of its <span class="math inline">k</span> nearest neighbours belong to (for <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>classification</strong></span> problems.)</span><br>
1617</li>
1618<li class="fragment"><span style="font-size: 80%;">Generate a prediction of <strong><span class="math inline">Y</span></strong> by taking the average of the target variable for the new observation’s <span class="math inline">k</span> nearest neighbours (for <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>regression</strong></span> problems.)</span><br>
1619</li>
1620</ul></li>
1621</ul></li>
1622</ul>
1623</section>
1624<section id="knn-in-python" class="slide level2">
1625<h2>KNN in Python <svg aria-hidden="true" role="img" viewbox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#5d3c85;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"></path></svg></h2>
1626<div style="margin-top: 90px; text-align: center;">
1627<div class="callout callout-tip callout-titled callout-style-default">
1628<div class="callout-body">
1629<div class="callout-title">
1630<div class="callout-icon-container">
1631<i class="callout-icon"></i>
1632</div>
1633<p><strong>Note</strong></p>
1634</div>
1635<div class="callout-content">
1636<p>The rest of today’s session will take place in <a href="https://github.com/sakeefkarim/soci-269-f25/blob/main/code/week%2012/soci_269_w12.ipynb" class="external" target="_blank"><span style="color: #5d3c85;"><i class="fa-brands fa-google" aria-label="google"></i></span> Colab</a>.</p>
1637</div>
1638</div>
1639</div>
1640</div>
1641<div class="invisible">
1642<p><span class="citation" data-cites="mckinney_python_2022">(<a href="#/references" role="doc-biblioref" onclick="">McKinney 2022</a>)</span></p>
1643</div>
1644</section></section>
1645<section>
1646<section id="an-open-work-session-november-19th" class="title-slide slide level1 center" data-background-color="#43464B">
1647<h1>An Open Work Session–<br>November 19<span style="font-size: 70%;"><sup>th</sup></span></h1>
1648
1649</section>
1650<section id="reminders-4" class="slide level2">
1651<h2>Reminders</h2>
1652<div class="centre-text">
1653<p>You have a <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>bit</em></strong></span> more time with<br>the second coding assignment.</p>
1654</div>
1655</section>
1656<section id="reminders-5" class="slide level2">
1657<h2>Reminders</h2>
1658<div style="margin-top: 90px; text-align: center;">
1659<div class="callout callout-warning callout-titled callout-style-default">
1660<div class="callout-body">
1661<div class="callout-title">
1662<div class="callout-icon-container">
1663<i class="callout-icon"></i>
1664</div>
1665<p><strong>Second Coding Assignment <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>Soft</em></strong></span> Deadline</strong></p>
1666</div>
1667<div class="callout-content">
1668<p>Your second coding assignment is due by <strong>8:00 PM</strong>
1668 on Friday, November 21<span style="font-size:70%"><sup>st</sup></span>.<br>This is a <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>soft</em></strong></span> deadline.</p>
1669</div>
1670</div>
1671</div>
1672</div>
1673</section>
1674<section id="reminders-6" class="slide level2">
1675<h2>Reminders</h2>
1676<div style="margin-top: 90px; text-align: center;">
1677<div class="callout callout-important callout-titled callout-style-default">
1678<div class="callout-body">
1679<div class="callout-title">
1680<div class="callout-icon-container">
1681<i class="callout-icon"></i>
1682</div>
1683<p><strong>Second Coding Assignment <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>Hard</em></strong></span> Deadline</strong></p>
1684</div>
1685<div class="callout-content">
1686<p>All coding assignments (in <code>Python</code> <svg aria-hidden="true" role="img" viewbox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#5d3c85;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"></path></svg>) <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>must</em></strong></span> be submitted by <strong>8:00 PM</strong> on Wednesday, December 3<span style="font-size:70%"><sup>rd</sup></span>.</p>
1687</div>
1688</div>
1689</div>
1690</div>
1691</section>
1692<section id="reminders-7" class="slide level2">
1693<h2>Reminders</h2>
1694<div style="text-align: center; margin-top: 25px;">
1695<p>Once again, assignment instructions are available online.</p>
1696<p align="center">
1697<iframe class="iframe" src="https://soci-269-a2.netlify.app/" height="450" width="1040" style="border: 1px solid #2e3846;">
1698</iframe>
1699</p>
1700<div style="text-align: center; font-size: 75%; margin-top:-40px">
1701<p><a href="https://soci-269-a1.netlify.app/" data-preview-link="true">Full Page</a></p>
1702</div>
1703</div>
1704</section>
1705<section id="reminders-8" class="slide level2">
1706<h2>Reminders</h2>
1707<h4 id="final-presentation-and-term-paper-1"><span style="color:#a9a9a9;">Final Presentation and Term Paper</span></h4>
1708<div style="text-align: center; font-size: 87%; margin-top: 20px; margin-bottom: 20px;">
1709<p>Guidelines for your <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>final presentation</em></strong></span> and <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>term paper</em></strong></span> are online.</p>
1710<p align="center">
1711<iframe class="iframe" src="https://soci-269-final.netlify.app/" height="450" width="1040" style="border: 1px solid #2e3846;">
1712</iframe>
1713</p>
1714<div style="text-align: center; font-size: 60%; margin-top:-40px">
1715<p><a href="https://soci-269-final.netlify.app/" data-preview-link="true">Full Page</a></p>
1716</div>
1717</div>
1718</section>
1719<section id="reminders-9" class="slide level2">
1720<h2>Reminders</h2>
1721<div class="centre-text">
1722<p>You can find out when you’re presenting<br>via <a href="https://moodle.amherst.edu/mod/resource/view.php?id=982310">Moodle</a>.</p>
1723</div>
1724</section>
1725<section id="an-update-1" class="slide level2">
1726<h2>An Update</h2>
1727<div class="centre-text">
1728<p>A quick note on <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>grades</em></strong></span>—and an opportunity for <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>resubmitting</em></strong></span> or<br><span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong><em>re-weighting</em></strong></span> a coding assignment.</p>
1729</div>
1730</section></section>
1731<section>
1732<section id="work-session" class="title-slide slide level1 center" data-background-color="#43464B">
1733<h1>Work Session</h1>
1734
1735</section>
1736<section id="create-a-pitch" class="slide level2">
1737<h2>Create a Pitch</h2>
1738<div style="margin-top: 80px; text-align: center;">
1739<p>Carefully review the guidelines for your term paper: i.e., scan the codebooks—or leverage the interactive tables—<a href="https://soci-269-final.netlify.app/" class="external" target="_blank">embedded online</a> to isolate variables of interest. How can you connect these items to the substantive material covered in&nbsp; <a href="https://soci-269.netlify.app/#modulei" class="external" target="_blank"><span style="font-size: 103%;"><code>Module I</code></span></a> &nbsp;of this class?</p>
1740<div style="margin-top: 40px;">
1741
1742</div>
1743<div class="fragment">
1744<p>Towards the end of class, we’ll hold a <span style="color: white; background-color: #5d3c85; padding: 1px 5px; border: 0.001px solid white; border-radius: 5px;"><strong>brainstorming</strong></span> session where everyone will <em>pitch</em> their preliminary ideas.</p>
1745</div>
1746</div>
1747</section></section>
1748<section>
1749<section id="enjoy-the-break" class="title-slide slide level1 center" data-background-color="#43464B">
1750<h1>Enjoy the Break</h1>
1751
1752</section>
1753<section id="references" class="slide level2 smaller scrollable">
1754<h2>References</h2>
1755<div style="text-align: center; margin-bottom: 15px">
1756<p><span style="color: #9c9c9c; font-size:95%;"><strong>Note</strong>: Scroll <span style="color: #5d3c85;"><i class="fa-solid fa-circle-chevron-down" aria-label="circle-chevron-down"></i></span> to access the entire bibliography</span></p>
1757</div>
1758
1759<div id="refs" class="references csl-bib-body hanging-indent" role="list">
1760<div id="ref-breiman2001" class="csl-entry" role="listitem">
1761Breiman, Leo. 2001. <span>“Statistical Modeling: The Two Cultures (with Comments and a Rejoinder by the Author).”</span> <em>Statistical Science</em> 16(3). doi: <a href="https://doi.org/10.1214/ss/1009213726">10.1214/ss/1009213726</a>.
1762</div>
1763<div id="ref-donoho2017" class="csl-entry" role="listitem">
1764Donoho, David. 2017. <span>“50 Years of Data Science.”</span> <em>Journal of Computational and Graphical Statistics</em> 26(4):745–66. doi: <a href="https://doi.org/10.1080/10618600.2017.1384734">10.1080/10618600.2017.1384734</a>.
1765</div>
1766<div id="ref-grimmer2021" class="csl-entry" role="listitem">
1767Grimmer, Justin, Margaret E. Roberts, and Brandon M. Stewart. 2021. <span>“Machine Learning for Social Science: An Agnostic Approach.”</span> <em>Annual Review of Political Science</em> 24(1):395–419. doi: <a href="https://doi.org/10.1146/annurev-polisci-053119-015921">10.1146/annurev-polisci-053119-015921</a>.
1768</div>
1769<div id="ref-lundberg_researcher_2022" class="csl-entry" role="listitem">
1770Lundberg, Ian, Jennie E. Brand, and Nanum Jeon. 2022. <span>“Researcher Reasoning Meets Computational Capacity: <span>Machine</span> Learning for Social Science.”</span> <em>Social Science Research</em> 108:102807. doi: <a href="https://doi.org/10.1016/j.ssresearch.2022.102807">10.1016/j.ssresearch.2022.102807</a>.
1771</div>
1772<div id="ref-mckinney_python_2022" class="csl-entry" role="listitem">
1773McKinney, Wes. 2022. <em><a href="https://wesmckinney.com/book/">Python for Data Analysis: Data Wrangling with Pandas, <span>NumPy</span>, and <span>Jupyter</span></a></em>. 3rd Edition. Sebastopol, CA: O’Reilly.
1774</div>
1775<div id="ref-molina_machine_2019" class="csl-entry" role="listitem">
1776Molina, Mario, and Filiz Garip. 2019. <span>“Machine <span>Learning</span> for <span>Sociology</span>.”</span> <em>Annual Review of Sociology</em> 45(Volume 45, 2019):27–45. doi: <a href="https://doi.org/10.1146/annurev-soc-073117-041106">10.1146/annurev-soc-073117-041106</a>.
1777</div>
1778<div id="ref-loon_machine_2022" class="csl-entry" role="listitem">
1779van Loon, Austin. 2022. <span>“<a href="https://doi.org/10.31235/osf.io/s7uer">Machine Learning and Deductive Social Science: An Introduction to Predictability Hypotheses</a>.”</span>
1780</div>
1781</div>
1782</section></section>
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2108          const config = {
2109            allowHTML: true,
2110            maxWidth: 500,
2111            delay: 100,
2112            arrow: false,
2113            appendTo: function(el) {
2114                return el.closest('section.slide') || el.parentElement;
2115            },
2116            interactive: true,
2117            interactiveBorder: 10,
2118            theme: 'light-border',
2119            placement: 'bottom-start',
2120          };
2121          if (contentFn) {
2122            config.content = contentFn;
2123          }
2124          if (onTriggerFn) {
2125            config.onTrigger = onTriggerFn;
2126          }
2127          if (onUntriggerFn) {
2128            config.onUntrigger = onUntriggerFn;
2129          }
2130            config['offset'] = [0,0];
2131            config['maxWidth'] = 700;
2132          window.tippy(el, config); 
2133        }
2134        const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
2135        for (var i=0; i<noterefs.length; i++) {
2136          const ref = noterefs[i];
2137          tippyHover(ref, function() {
2138            // use id or data attribute instead here
2139            let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
2140            try { href = new URL(href).hash; } catch {}
2141            const id = href.replace(/^#\/?/, "");
2142            const note = window.document.getElementById(id);
2143            if (note) {
2144              return note.innerHTML;
2145            } else {
2146              return "";
2147            }
2148          });
2149        }
2150            let selectedAnnoteEl;
2151            const selectorForAnnotation = ( cell, annotation) => {
2152              let cellAttr = 'data-code-cell="' + cell + '"';
2153              let lineAttr = 'data-code-annotation="' +  annotation + '"';
2154              const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
2155              return selector;
2156            }
2157            const selectCodeLines = (annoteEl) => {
2158              const doc = window.document;
2159              const targetCell = annoteEl.getAttribute("data-target-cell");
2160              const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
2161              const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
2162              const lines = annoteSpan.getAttribute("data-code-lines").split(",");
2163              const lineIds = lines.map((line) => {
2164                return targetCell + "-" + line;
2165              })
2166              let top = null;
2167              let height = null;
2168              let parent = null;
2169              if (lineIds.length > 0) {
2170                  //compute the position of the single el (top and bottom and make a div)
2171                  const el = window.document.getElementById(lineIds[0]);
2172                  top = el.offsetTop;
2173                  height = el.offsetHeight;
2174                  parent = el.parentElement.parentElement;
2175                if (lineIds.length > 1) {
2176                  const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
2177                  const bottom = lastEl.offsetTop + lastEl.offsetHeight;
2178                  height = bottom - top;
2179                }
2180                if (top !== null && height !== null && parent !== null) {
2181                  // cook up a div (if necessary) and position it 
2182                  let div = window.document.getElementById("code-annotation-line-highlight");
2183                  if (div === null) {
2184                    div = window.document.createElement("div");
2185                    div.setAttribute("id", "code-annotation-line-highlight");
2186                    div.style.position = 'absolute';
2187                    parent.appendChild(div);
2188                  }
2189                  div.style.top = top - 2 + "px";
2190                  div.style.height = height + 4 + "px";
2191                  div.style.left = 0;
2192                  let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
2193                  if (gutterDiv === null) {
2194                    gutterDiv = window.document.createElement("div");
2195                    gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
2196                    gutterDiv.style.position = 'absolute';
2197                    const codeCell = window.document.getElementById(targetCell);
2198                    const gutter = codeCell.querySelector('.code-annotation-gutter');
2199                    gutter.appendChild(gutterDiv);
2200                  }
2201                  gutterDiv.style.top = top - 2 + "px";
2202                  gutterDiv.style.height = height + 4 + "px";
2203                }
2204                selectedAnnoteEl = annoteEl;
2205              }
2206            };
2207            const unselectCodeLines = () => {
2208              const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
2209              elementsIds.forEach((elId) => {
2210                const div = window.document.getElementById(elId);
2211                if (div) {
2212                  div.remove();
2213                }
2214              });
2215              selectedAnnoteEl = undefined;
2216            };
2217              // Handle positioning of the toggle
2218          window.addEventListener(
2219            "resize",
2220            throttle(() => {
2221              elRect = undefined;
2222              if (selectedAnnoteEl) {
2223                selectCodeLines(selectedAnnoteEl);
2224              }
2225            }, 10)
2226          );
2227          function throttle(fn, ms) {
2228          let throttle = false;
2229          let timer;
2230            return (...args) => {
2231              if(!throttle) { // first call gets through
2232                  fn.apply(this, args);
2233                  throttle = true;
2234              } else { // all the others get throttled
2235                  if(timer) clearTimeout(timer); // cancel #2
2236                  timer = setTimeout(() => {
2237                    fn.apply(this, args);
2238                    timer = throttle = false;
2239                  }, ms);
2240              }
2241            };
2242          }
2243            const annoteTargets = window.document.querySelectorAll('.code-annotation-anchor');
2244            for (let i=0; i<annoteTargets.length; i++) {
2245              const annoteTarget = annoteTargets[i];
2246              const targetCell = annoteTarget.getAttribute("data-target-cell");
2247              const targetAnnotation = annoteTarget.getAttribute("data-target-annotation");
2248              const contentFn = () => {
2249                const content = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
2250                if (content) {
2251                  const tipContent = content.cloneNode(true);
2252                  tipContent.classList.add("code-annotation-tip-content");
2253                  return tipContent.outerHTML;
2254                }
2255              }
2256              const config = {
2257                allowHTML: true,
2258                content: contentFn,
2259                onShow: (instance) => {
2260                  selectCodeLines(instance.reference);
2261                  instance.reference.classList.add('code-annotation-active');
2262                  window.tippy.hideAll();
2263                },
2264                onHide: (instance) => {
2265                  unselectCodeLines();
2266                  instance.reference.classList.remove('code-annotation-active');
2267                },
2268                maxWidth: 300,
2269                delay: [50, 0],
2270                duration: [200, 0],
2271                offset: [5, 10],
2272                arrow: true,
2273                trigger: 'click',
2274                appendTo: function(el) {
2275                  return el.parentElement.parentElement.parentElement;
2276                },
2277                interactive: true,
2278                interactiveBorder: 10,
2279                theme: 'light-border',
2280                placement: 'right',
2281                positionFixed: true,
2282                popperOptions: {
2283                  modifiers: [
2284                  {
2285                    name: 'flip',
2286                    options: {
2287                      flipVariations: false, // true by default
2288                      allowedAutoPlacements: ['right'],
2289                      fallbackPlacements: ['right', 'top', 'top-start', 'top-end', 'bottom', 'bottom-start', 'bottom-end', 'left'],
2290                    },
2291                  },
2292                  {
2293                    name: 'preventOverflow',
2294                    options: {
2295                      mainAxis: false,
2296                      altAxis: false
2297                    }
2298                  }
2299                  ]        
2300                }      
2301              };
2302              window.tippy(annoteTarget, config); 
2303            }
2304        const findCites = (el) => {
2305          const parentEl = el.parentElement;
2306          if (parentEl) {
2307            const cites = parentEl.dataset.cites;
2308            if (cites) {
2309              return {
2310                el,
2311                cites: cites.split(' ')
2312              };
2313            } else {
2314              return findCites(el.parentElement)
2315            }
2316          } else {
2317            return undefined;
2318          }
2319        };
2320        var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
2321        for (var i=0; i<bibliorefs.length; i++) {
2322          const ref = bibliorefs[i];
2323          const citeInfo = findCites(ref);
2324          if (citeInfo) {
2325            tippyHover(citeInfo.el, function() {
2326              var popup = window.document.createElement('div');
2327              citeInfo.cites.forEach(function(cite) {
2328                var citeDiv = window.document.createElement('div');
2329                citeDiv.classList.add('hanging-indent');
2330                citeDiv.classList.add('csl-entry');
2331                var biblioDiv = window.document.getElementById('ref-' + cite);
2332                if (biblioDiv) {
2333                  citeDiv.innerHTML = biblioDiv.innerHTML;
2334                }
2335                popup.appendChild(citeDiv);
2336              });
2337              return popup.innerHTML;
2338            });
2339          }
2340        }
2341      });
2342      </script>
2342
2343    
2343<script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","descPosition":"bottom","loop":false,"openEffect":"zoom","selector":".lightbox"});
2344    (function() {
2345      let previousOnload = window.onload;
2346      window.onload = () => {
2347        if (previousOnload) {
2348          previousOnload();
2349        }
2350        lightboxQuarto.on('slide_before_load', (data) => {
2351          const { slideIndex, slideNode, slideConfig, player, trigger } = data;
2352          const href = trigger.getAttribute('href');
2353          if (href !== null) {
2354            const imgEl = window.document.querySelector(`a[href="${href}"] img`);
2355            if (imgEl !== null) {
2356              const srcAttr = imgEl.getAttribute("src");
2357              if (srcAttr && srcAttr.startsWith("data:")) {
2358                slideConfig.href = srcAttr;
2359              }
2360            }
2361          } 
2362        });
2363      
2364        lightboxQuarto.on('slide_after_load', (data) => {
2365          const { slideIndex, slideNode, slideConfig, player, trigger } = data;
2366          if (window.Quarto?.typesetMath) {
2367            window.Quarto.typesetMath(slideNode);
2368          }
2369        });
2370      
2371      };
2372      
2373    })();
2374              </script>
2374
2375    
2376
2377</body></html>

Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.