matbench docbot: rebuild pages

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@@ -1218,7 +1218,7 @@ <h3 id="general-purpose-algorithms">General purpose algorithms</h3>
 <li>All 10 regression tasks are recorded, OR...</li>
 <li>All 9 structure tasks are recorded. If only the 9 structure tasks are recorded, the algorithm is marked with "requires structure".</li>
 </ul>
-<p>General purpose algorithms' results will appear on both the <a href="../..">General Purpose Leaderboard</a> as well as the Task-specific leaderboards.</p>
+<p>General purpose algorithms' results will appear on both the <a href="../../">General Purpose Leaderboard</a> as well as the Task-specific leaderboards.</p>
 <h3 id="task-specific-algorithms">Task specific algorithms</h3>
 <p>Task-specific algorithms can fit on any subset of tasks; for example, a single task. Task-specific algorithms may be valid or specialized only for a subset of the tasks in the benchmark.  </p>
 <p>For example, if you have a model which was specifically created for predicting bulk metallic glasses, you may submit a benchmark containing only results for the <code>matbench_glass</code> dataset. </p>

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docs/Full Benchmark Data/matbench_v0.1_rf/index.html

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@@ -2129,7 +2129,7 @@
 
 <h1 id="matbench_v01-rf-scmmagpie">matbench_v0.1: RF-SCM/Magpie</h1>
 <h3 id="algorithm-description">Algorithm description:</h3>
-<p>A random forest using features from the Sine Coulomb Matrix and MagPie featurization algorthims. Sine Coulomb Matrix creates structural features based on Coulombic interactions inside a periodic boundary condition (i.e., for crystalline materials with known structure). MagPie features are weighted elemental features based on elemental data such as electronegativity, melting point, and electron affinity. Algorithms were run inside of the Automatminer v1.0.3.20191111 framework for convenience, though no auto-featurization or AutoML were run. Data cleaning dropped features with more than 1% nan samples, imputing missing samples using the mean of the training data. No feature reduction was performed. Both featurization techniques were applied to structure problems, only MagPie features were applied to problems without structure. Random forest uses 500 estimators.</p>
+<p>A random forest using features from the Sine Coulomb Matrix and MagPie featurization algorithms. Sine Coulomb Matrix creates structural features based on Coulombic interactions inside a periodic boundary condition (i.e., for crystalline materials with known structure). MagPie features are weighted elemental features based on elemental data such as electronegativity, melting point, and electron affinity. Algorithms were run inside of the Automatminer v1.0.3.20191111 framework for convenience, though no auto-featurization or AutoML were run. Data cleaning dropped features with more than 1% nan samples, imputing missing samples using the mean of the training data. No feature reduction was performed. Both featurization techniques were applied to structure problems, only MagPie features were applied to problems without structure. Random forest uses 500 estimators.</p>
 <h4 id="notes">Notes:</h4>
 <p>No hyperparameter tuning was performed on the RF, as a large, constant number of trees were used in constructing each fold's model; the entire training+validation set was used as training data for the RF.</p>
 <p>Raw data download and example notebook available <a href="https://github.com/hackingmaterials/matbench/tree/main/benchmarks/matbench_v0.1_rf">on the matbench repo</a>.</p>

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@@ -1269,7 +1269,7 @@ <h2 id="step-1-create-3-required-files">Step 1: Create 3 required files</h2>
 </ol>
 <h2 id="step-2-put-files-in-appropriate-folder">Step 2: Put files in appropriate folder</h2>
 <ul>
-<li>a. If you are using matbench through pypi, clone the source repository in order to make a pull request. Find instrucions for cloning the source repository on the <a href="../1install/">Installation page</a>.</li>
+<li>a. If you are using matbench through pypi, clone the source repository in order to make a pull request. Find instructions for cloning the source repository on the <a href="../1install/">Installation page</a>.</li>
 <li>b. Locate the <code>matbench/benchmarks</code> directory.</li>
 <li>c. Create a new directory <code>&lt;benchmark name&gt;_&lt;algorithm name&gt;</code> according to your algorithm and the benchmark you ran (e.g., <code>matbench_v0.1_my_algorithm_namev2</code>).</li>
 <li>d. Put the required files from <a href="#step-1-create-3-required-files">Step 1</a> into this directory.</li>
@@ -1324,14 +1324,14 @@ <h2 id="source-files">Source files</h2>
 <h3 id="general-guidelines">General guidelines</h3>
 <p>The source file should contain all the code needed for configuring, training, and running your algorithm on all the benchmark tasks you decide to run.</p>
 <p>The easiest way to create a source file is just use the source file you used while <a href="../2run/">recording your benchmark results</a>.</p>
-<p>There are no naming requirements for these source files. You can also include other supporting files, like metadata, features, etc. if they are critical for the algorithm to run and they are small (&lt;10MB). </p>
+<p>There are no naming requirements for these source files. You can also include other supporting files, like metadata, features, etc. if they are critical for the algorithm to run and they are small (&lt;10MB).</p>
 <p>Please include the matbench code (e.g., <code>mb.record(...)</code>) for obtaining benchmarks/recording/examining results in the source files.</p>
 <h3 id="jupyter-notebooks">Jupyter notebooks</h3>
 <p>The preferred format for source files is a <strong>jupyter notebook</strong> with some code for running your algorithm on Matbench. You can find an example</p>
 <p><img alt="example_notebook" src="../../static/notebook_example.png" /></p>
-<p>The notebook should generally follow the format of the example notebook <code>/benchmarks/matbench_v0.1_dummy/notebook.ipynb</code>. Try to include a long form, human readable description of how your algorithm works, any package versions needed to have it run correctly, and <strong>most importantly, a link to a publication for your algorithm</strong>. </p>
+<p>The notebook should generally follow the format of the example notebook <code>/benchmarks/matbench_v0.1_dummy/notebook.ipynb</code>. Try to include a long form, human readable description of how your algorithm works, any package versions needed to have it run correctly, and <strong>most importantly, a link to a publication for your algorithm</strong>.</p>
 <p>Aside from that, what goes in your notebook is pretty freeform; <strong>put whatever is needed to
-allow someone else to train and run your algorithm on the benchmark</strong>. </p>
+allow someone else to train and run your algorithm on the benchmark</strong>.</p>
 <p>You can find an example template for a notebook in the matbench repo under <code>/benchmarks/matbench_v0.1_dummy/notebook.ipynb</code></p>
 <h3 id="py-files"><code>.py</code> files</h3>
 <p>If you use <code>.py</code> files as source in the submission, please comment your code as much as you can to help others run it!</p>

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