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    "source": [
     "### Labels and stimulus annotations\n",
     "\n",
-    "As mentioned in prior sessions (e.g.[Supervised learning using scikit-learn](https://main-educational.github.io/material.html#supervised-learning-using-scikit-learn) and hinted at the [beginning of this session](#A-short-primer-on-datasets), when working on a `supervised learning problem`, we also need the `ground truth`/`true labels` for each `sample`. Why? Because we need to evaluate how a given `model` performs via comparing the `labels` it `predicted` to the `true labels`. What these `labels` refer to can be manifold and of course depends on the `task` at hand. \n",
+    "As mentioned in prior sessions (e.g.[Supervised learning using scikit-learn](https://main-educational.github.io/material.html#supervised-learning-using-scikit-learn) and hinted at the [beginning of this session](#A-short-primer-on-datasets)), when working on a `supervised learning problem`, we also need the `ground truth`/`true labels` for each `sample`. Why? Because we need to evaluate how a given `model` performs via comparing the `labels` it `predicted` to the `true labels`. What these `labels` refer to can be manifold and of course depends on the `task` at hand. \n",
     "\n",
     "For example, a `supervised learning problem` in the `dataset` at hand could entail `training` a `model` to `recognize` and `predict` what `category` `participants` perceived based on their `brain activation`. Thus, we would need to know what `category` was shown when during the acquisition of the `data` (or which `category` resulted in which `estimated` `brain activity`). \n",
     "\n",

encoding.html

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gcn_decoding.html

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 Dataset created in ../data/haxby2001
 
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haxby_data.html

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@@ -2636,7 +2636,7 @@ <h3>Neuroimaging files<a class="headerlink" href="#neuroimaging-files" title="Li
 </section>
 <section id="labels-and-stimulus-annotations">
 <h3>Labels and stimulus annotations<a class="headerlink" href="#labels-and-stimulus-annotations" title="Link to this heading">#</a></h3>
-<p>As mentioned in prior sessions (e.g.<a class="reference external" href="https://main-educational.github.io/material.html#supervised-learning-using-scikit-learn">Supervised learning using scikit-learn</a> and hinted at the <a class="reference internal" href="#A-short-primer-on-datasets"><span class="xref myst">beginning of this session</span></a>, when working on a <code class="docutils literal notranslate"><span class="pre">supervised</span> <span class="pre">learning</span> <span class="pre">problem</span></code>, we also need the <code class="docutils literal notranslate"><span class="pre">ground</span> <span class="pre">truth</span></code>/<code class="docutils literal notranslate"><span class="pre">true</span> <span class="pre">labels</span></code> for each <code class="docutils literal notranslate"><span class="pre">sample</span></code>. Why? Because we need to evaluate how a given <code class="docutils literal notranslate"><span class="pre">model</span></code> performs via comparing the <code class="docutils literal notranslate"><span class="pre">labels</span></code> it <code class="docutils literal notranslate"><span class="pre">predicted</span></code> to the <code class="docutils literal notranslate"><span class="pre">true</span> <span class="pre">labels</span></code>. What these <code class="docutils literal notranslate"><span class="pre">labels</span></code> refer to can be manifold and of course depends on the <code class="docutils literal notranslate"><span class="pre">task</span></code> at hand.</p>
+<p>As mentioned in prior sessions (e.g.<a class="reference external" href="https://main-educational.github.io/material.html#supervised-learning-using-scikit-learn">Supervised learning using scikit-learn</a> and hinted at the <a class="reference internal" href="#A-short-primer-on-datasets"><span class="xref myst">beginning of this session</span></a>), when working on a <code class="docutils literal notranslate"><span class="pre">supervised</span> <span class="pre">learning</span> <span class="pre">problem</span></code>, we also need the <code class="docutils literal notranslate"><span class="pre">ground</span> <span class="pre">truth</span></code>/<code class="docutils literal notranslate"><span class="pre">true</span> <span class="pre">labels</span></code> for each <code class="docutils literal notranslate"><span class="pre">sample</span></code>. Why? Because we need to evaluate how a given <code class="docutils literal notranslate"><span class="pre">model</span></code> performs via comparing the <code class="docutils literal notranslate"><span class="pre">labels</span></code> it <code class="docutils literal notranslate"><span class="pre">predicted</span></code> to the <code class="docutils literal notranslate"><span class="pre">true</span> <span class="pre">labels</span></code>. What these <code class="docutils literal notranslate"><span class="pre">labels</span></code> refer to can be manifold and of course depends on the <code class="docutils literal notranslate"><span class="pre">task</span></code> at hand.</p>
 <p>For example, a <code class="docutils literal notranslate"><span class="pre">supervised</span> <span class="pre">learning</span> <span class="pre">problem</span></code> in the <code class="docutils literal notranslate"><span class="pre">dataset</span></code> at hand could entail <code class="docutils literal notranslate"><span class="pre">training</span></code> a <code class="docutils literal notranslate"><span class="pre">model</span></code> to <code class="docutils literal notranslate"><span class="pre">recognize</span></code> and <code class="docutils literal notranslate"><span class="pre">predict</span></code> what <code class="docutils literal notranslate"><span class="pre">category</span></code> <code class="docutils literal notranslate"><span class="pre">participants</span></code> perceived based on their <code class="docutils literal notranslate"><span class="pre">brain</span> <span class="pre">activation</span></code>. Thus, we would need to know what <code class="docutils literal notranslate"><span class="pre">category</span></code> was shown when during the acquisition of the <code class="docutils literal notranslate"><span class="pre">data</span></code> (or which <code class="docutils literal notranslate"><span class="pre">category</span></code> resulted in which <code class="docutils literal notranslate"><span class="pre">estimated</span></code> <code class="docutils literal notranslate"><span class="pre">brain</span> <span class="pre">activity</span></code>).</p>
 <p>Within our <code class="docutils literal notranslate"><span class="pre">tutorial</span> <span class="pre">dataset</span></code>, this information is included in the <code class="docutils literal notranslate"><span class="pre">session_target</span></code> file. Using <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/index.html">pandas</a> we can easily <code class="docutils literal notranslate"><span class="pre">load</span></code> and <code class="docutils literal notranslate"><span class="pre">inspect</span></code> this file:</p>
 <div class="cell docutils container">