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<li><a class="reference internal" href="#">5. <code class="docutils literal notranslate"><span class="pre">skopt</span></code>’s top level minimization functions</a><ul>
<li><a class="reference internal" href="#dummy-minimize">5.1. <code class="xref py py-class docutils literal notranslate"><span class="pre">dummy_minimize</span></code></a></li>
<li><a class="reference internal" href="#forest-minimize">5.2. <code class="xref py py-class docutils literal notranslate"><span class="pre">forest_minimize</span></code></a></li>
<li><a class="reference internal" href="#gbrt-minimize">5.3. <code class="xref py py-class docutils literal notranslate"><span class="pre">gbrt_minimize</span></code></a></li>
<li><a class="reference internal" href="#gp-minimize">5.4. <code class="xref py py-class docutils literal notranslate"><span class="pre">gp_minimize</span></code></a></li>
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<section id="skopt-s-top-level-minimization-functions">
<span id="minimize-functions"></span><h1><span class="section-number">5. </span><code class="docutils literal notranslate"><span class="pre">skopt</span></code>’s top level minimization functions<a class="headerlink" href="#skopt-s-top-level-minimization-functions" title="Permalink to this headline">¶</a></h1>
<p>These are easy to get started with. They mirror the <code class="docutils literal notranslate"><span class="pre">scipy.optimize</span></code>
API and provide a high level interface to various pre-configured
optimizers.</p>
<section id="dummy-minimize">
<h2><span class="section-number">5.1. </span><a class="reference internal" href="generated/skopt.dummy_minimize.html#skopt.dummy_minimize" title="skopt.dummy_minimize"><code class="xref py py-class docutils literal notranslate"><span class="pre">dummy_minimize</span></code></a><a class="headerlink" href="#dummy-minimize" title="Permalink to this headline">¶</a></h2>
<p>Random search by uniform sampling within the given bounds.</p>
</section>
<section id="forest-minimize">
<h2><span class="section-number">5.2. </span><a class="reference internal" href="generated/skopt.forest_minimize.html#skopt.forest_minimize" title="skopt.forest_minimize"><code class="xref py py-class docutils literal notranslate"><span class="pre">forest_minimize</span></code></a><a class="headerlink" href="#forest-minimize" title="Permalink to this headline">¶</a></h2>
<p>Sequential optimisation using decision trees.</p>
<p>A tree based regression model is used to model the expensive to evaluate
function <code class="docutils literal notranslate"><span class="pre">func</span></code>. The model is improved by sequentially evaluating
the expensive function at the next best point. Thereby finding the
minimum of <code class="docutils literal notranslate"><span class="pre">func</span></code> with as few evaluations as possible.</p>
</section>
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<h2><span class="section-number">5.3. </span><a class="reference internal" href="generated/skopt.gbrt_minimize.html#skopt.gbrt_minimize" title="skopt.gbrt_minimize"><code class="xref py py-class docutils literal notranslate"><span class="pre">gbrt_minimize</span></code></a><a class="headerlink" href="#gbrt-minimize" title="Permalink to this headline">¶</a></h2>
<p>Sequential optimization using gradient boosted trees.</p>
<p>Gradient boosted regression trees are used to model the (very)
expensive to evaluate function <code class="docutils literal notranslate"><span class="pre">func</span></code>. The model is improved
by sequentially evaluating the expensive function at the next
best point. Thereby finding the minimum of <code class="docutils literal notranslate"><span class="pre">func</span></code> with as
few evaluations as possible.</p>
</section>
<section id="gp-minimize">
<h2><span class="section-number">5.4. </span><a class="reference internal" href="generated/skopt.gp_minimize.html#skopt.gp_minimize" title="skopt.gp_minimize"><code class="xref py py-class docutils literal notranslate"><span class="pre">gp_minimize</span></code></a><a class="headerlink" href="#gp-minimize" title="Permalink to this headline">¶</a></h2>
<p>Bayesian optimization using Gaussian Processes.</p>
<p>If every function evaluation is expensive, for instance
when the parameters are the hyperparameters of a neural network
and the function evaluation is the mean cross-validation score across
ten folds, optimizing the hyperparameters by standard optimization
routines would take for ever!</p>
<p>The idea is to approximate the function using a Gaussian process.
In other words the function values are assumed to follow a multivariate
gaussian. The covariance of the function values are given by a
GP kernel between the parameters. Then a smart choice to choose the
next parameter to evaluate can be made by the acquisition function
over the Gaussian prior which is much quicker to evaluate.</p>
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