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<section id="root-mean-squared-error">
<h1>root_mean_squared_error<a class="headerlink" href="#root-mean-squared-error" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.metrics.root_mean_squared_error">
<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">root_mean_squared_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/metrics/_regression.py#L522"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.root_mean_squared_error" title="Link to this definition">#</a></dt>
<dd><p>Root mean squared error regression loss.</p>
<p>Read more in the <a class="reference internal" href="../model_evaluation.html#mean-squared-error"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.4.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>y_true</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>Ground truth (correct) target values.</p>
</dd>
<dt><strong>y_pred</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>Estimated target values.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
<dt><strong>multioutput</strong><span class="classifier">{‘raw_values’, ‘uniform_average’} or array-like of shape (n_outputs,), default=’uniform_average’</span></dt><dd><p>Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.</p>
<dl class="simple">
<dt>‘raw_values’ :</dt><dd><p>Returns a full set of errors in case of multioutput input.</p>
</dd>
<dt>‘uniform_average’ :</dt><dd><p>Errors of all outputs are averaged with uniform weight.</p>
</dd>
</dl>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>loss</strong><span class="classifier">float or ndarray of floats</span></dt><dd><p>A non-negative floating point value (the best value is 0.0), or an
array of floating point values, one for each individual target.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">root_mean_squared_error</span>
<span class="gp">>>> </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">root_mean_squared_error</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
<span class="go">0.612...</span>
<span class="gp">>>> </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],[</span><span class="mi">7</span><span class="p">,</span> <span class="o">-</span><span class="mi">6</span><span class="p">]]</span>
<span class="gp">>>> </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],[</span><span class="mi">8</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">]]</span>
<span class="gp">>>> </span><span class="n">root_mean_squared_error</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
<span class="go">0.822...</span>
</pre></div>
</div>
</dd></dl>
<section id="gallery-examples">
<h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link to this heading">#</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="histogram_based_gradient_boosting (HGBT) models may be one of the most useful supervised learning models in scikit-learn. They are based on a modern gradient boosting implementation comparable to LightGBM and XGBoost. As such, HGBT models are more feature rich than and often outperform alternative models like random forests, especially when the number of samples is larger than some ten thousands (see sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py)."><img alt="" src="../../_images/sphx_glr_plot_hgbt_regression_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_hgbt_regression.html#sphx-glr-auto-examples-ensemble-plot-hgbt-regression-py"><span class="std std-ref">Features in Histogram Gradient Boosting Trees</span></a></p>
<div class="sphx-glr-thumbnail-title">Features in Histogram Gradient Boosting Trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how Polars-engineered lagged features can be used for time series forecasting with HistGradientBoostingRegressor on the Bike Sharing Demand dataset."><img alt="" src="../../_images/sphx_glr_plot_time_series_lagged_features_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_time_series_lagged_features.html#sphx-glr-auto-examples-applications-plot-time-series-lagged-features-py"><span class="std std-ref">Lagged features for time series forecasting</span></a></p>
<div class="sphx-glr-thumbnail-title">Lagged features for time series forecasting</div>
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