alg-ml >> {ridgeBART}; miss >> resource; proj-dev >> git proj; quarto >> param render, output options; sql >> r permissions

Author: ercbk

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@@ -2056,7 +2062,7 @@ <h1 class="unnumbered">Preface</h1>
 <p>Initially, I had no intent of publicly releasing these notes, so I haven’t attributed all the authors of the content inside this notebook. If you see your work and want credit, please let me know.</p>
 <p>This is a live document that’s frequently updated.</p>
 <p>Also, if you see any mistakes or have any questions, please open an issue at the github <a href="https://github.com/ercbk/Data-Science-Notebook">repository</a>.</p>
-<p>Cover designed by Google Imagen 3</p>
+<p>Cover designed using Google Imagen 3</p>
 
 
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_book/qmd/_resources/Project,_Development.resources/git-proj-board-2.png

@@ -2451,6 +2451,12 @@ <h3 data-number="1.1.1" class="anchored" data-anchor-id="sec-alg-ml-trees-dt"><s
 <li>Peforms extremely well when the dataset contains many noise variables, and when significant high-order interactions are present alongside non-significant low-order interactions. In these scenarios, the proposed method outperforms other methods, including the random forest.</li>
 </ul></li>
 <li><span style="color: #990000">{</span><a href="https://cran.r-project.org/web/packages/SoftBart/index.html" style="color: #990000">SoftBart</a><span style="color: #990000">}</span> - Sparsity inducing soft decision trees. Overcomes smoothness and curse of dimensionality limitations of other tree methods</li>
+<li><span style="color: #990000">{</span><a href="https://github.com/ryanyee3/ridgeBART" style="color: #990000">ridgeBART</a><span style="color: #990000">}</span> (<a href="https://arxiv.org/abs/2411.07984">Paper</a>) - Uses trees that output linear combinations of ridge functions, which compose affine transformations of the inputs with a non-linearity, i.e.&nbsp;a Bayesian ensemble of localized neural networks with a single hidden layer.
+<ul>
+<li>Whereas the original BART model approximates functions with piecewise constant step functions, ridgeBART approximates functions with piecewise continuous functions</li>
+<li>Outperformed the original BART model and GP-based extensions in terms of predictive accuracy and computation time on both targeted smoothing and general regression problems.</li>
+<li>No variable selection</li>
+</ul></li>
 </ul></li>
 </ul></li>
 <li><u>Classification</u>
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