Модели StatSimhttps://statsim.com/models/ru/2020-11-30T20:00:00+01:00Синтетические данные Friedman-12020-11-30T20:00:00+01:002020-11-30T20:00:00+01:00Антон Землянскийtag:statsim.com,2020-11-30:/models/ru/friedman1/<p>Генерация искусственных данных на основе функции Friedman-1 воплощенной в StatSim</p><style type="text/css">/*!
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">iframer</span> <span class="kn">import</span> <span class="o">*</span>
<span class="n">iframer</span><span class="p">(</span><span class="s1">'https://statsim.com/app/?m=friedman1&preview=1'</span><span class="p">)</span>
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<a class="iframer-link" href="https://statsim.com/app/?m=friedman1&preview=1" target="_blank" style="margin: 10px 0;">Open in statsim.com</a>
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<h2 id="Модель-Friedman-1">Модель Friedman-1<a class="anchor-link" href="#Модель-Friedman-1">¶</a></h2><p>Синтетический датасет Friedman-1 был впервые описан в работе <a href="http://www.stat.yale.edu/~lc436/08Spring665/Mars_Friedman_91.pdf">Multivariate Adaptive Regression Splines</a> Джерома Фридмана в 1991. Все вводные параметры $\mathbf{x}$ являются независимыми случайными переменными равномерно распределенными на интервале от 0 до 1. Результат $y$ рассчитывается по формуле:</p>
$$ f(\mathbf{x}) = 10 sin(\pi x_1 x_2) + 20 (x_3 - 0.5)^2 + 10 x_4 + 5 x_5 + \mathcal{N}(0, sigma) $$<p></p>
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<p>По умолчанию, генерируются 10 различных переменных $\mathbf{x}$, из которых только первые 5 используются в формуле. Это делает такой датасет удобным для проверки методов отбора переменных.</p>
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<p>После успешной генерации, нажмите <strong>Download (CSV)</strong> для загрузки данных на локальный диск.</p>
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<p>Этот датасет также доступен в:</p>
<ul>
<li><a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman1.html">sklearn.datasets.make_friedman1</a> (Python)</li>
<li><a href="https://cran.r-project.org/web/packages/mlbench/index.html">MLBench</a> (R)</li>
<li><a href="https://statsim.com/gen/">StatSim Gen</a> (Web)</li>
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Friedman-1 synthetic dataset2020-11-30T20:00:00+01:002020-11-30T20:00:00+01:00Anton Zemlyanskytag:statsim.com,2020-11-30:/models/friedman1/<p>Generate a synthetic dataset using the Friedman-1 function implemented in StatSim</p><style type="text/css">/*!
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/* The following gets added to the <head> if it is detected that the user has a
* monospace font with inconsistent normal/bold/italic height. See
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* See: https://github.com/ipython/ipython/issues/1503
*
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/* Changed to auto to autogrow */
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/*
Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme
*/
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.highlight-qualifier {
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.highlight-bracket {
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/* Modern browsers */
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div.output_area {
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/* Old browsers */
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-moz-box-flex: 1;
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.rendered_html img,
.rendered_html img.unconfined,
.rendered_html * + .alert {
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[dir="rtl"]
div.text_cell {
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h5:hover .anchor-link,
h6:hover .anchor-link {
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.text_cell.rendered
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered
.text_cell.unrendered .text_cell_render {
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.text_cell .dropzone .input_area {
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.cm-header-1,
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</style>
<style type="text/css"> .highlight pre .hll { background-color: #ffffcc }
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [1]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">iframer</span> <span class="kn">import</span> <span class="o">*</span>
<span class="n">iframer</span><span class="p">(</span><span class="s1">'https://statsim.com/app/?m=friedman1&preview=1'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_html rendered_html output_subarea ">
<div class="iframer-wrapper" style="position: relative; padding-bottom: 550px; padding-top: 25px; height: 0;">
<iframe src="https://statsim.com/app/?m=friedman1&preview=1" style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;"></iframe>
</div><br>
<a class="iframer-link" href="https://statsim.com/app/?m=friedman1&preview=1" target="_blank" style="margin: 10px 0;">Open in statsim.com</a>
</div>
</div>
</div>
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<h2 id="Friedman-1-model">Friedman-1 model<a class="anchor-link" href="#Friedman-1-model">¶</a></h2><p>This dataset was described in the <a href="http://www.stat.yale.edu/~lc436/08Spring665/Mars_Friedman_91.pdf">Multivariate Adaptive Regression Splines</a> paper by Jerome H. Friedman in 1991. All features $\mathbf{x}$ are independent uniform random variables taking values from 0 to 1. The output $y$ is calculated with the formula:</p>
$$ f(\mathbf{x}) = 10 sin(\pi x_1 x_2) + 20 (x_3 - 0.5)^2 + 10 x_4 + 5 x_5 + \mathcal{N}(0, sigma) $$<p></p>
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<p>By default ten $\mathbf{x}$ variables are generated, from which only first 5 are used in the formula. That makes this dataset useful for testing feature selection methods too.</p>
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<p>After generating the data click <strong>Download (CSV)</strong> to download the dataset in the CSV format.</p>
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<p>Also availaible in:</p>
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<li><a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman1.html">sklearn.datasets.make_friedman1</a> (Python)</li>
<li><a href="https://cran.r-project.org/web/packages/mlbench/index.html">MLBench</a> (R)</li>
<li><a href="https://statsim.com/gen/">StatSim Gen</a> (Web)</li>
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