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<h1>Module <a href="type_Gpr_interfaces.Sigs.Eval.Stats.html">Gpr_interfaces.Sigs.Eval.Stats</a></h1>

<pre><span class="keyword">module</span> Stats: <code class="code">sig</code> <a href="Gpr_interfaces.Sigs.Eval.Stats.html">..</a> <code class="code">end</code></pre><div class="info module top">
Statistics derived from trained models<br>
</div>
<hr width="100%">

<pre><code><span id="TYPEt"><span class="keyword">type</span> <code class="type"></code>t</span> = {</code></pre><table class="typetable">
<tr>
<td align="left" valign="top" >
<code>&nbsp;&nbsp;</code></td>
<td align="left" valign="top" >
<code><span id="TYPEELTt.n_samples">n_samples</span>&nbsp;: <code class="type">int</code>;</code></td>
<td class="typefieldcomment" align="left" valign="top" ><code>(*</code></td><td class="typefieldcomment" align="left" valign="top" ><div class="info ">
Number of samples used for training<br>
</div>
</td><td class="typefieldcomment" align="left" valign="bottom" ><code>*)</code></td>
</tr>
<tr>
<td align="left" valign="top" >
<code>&nbsp;&nbsp;</code></td>
<td align="left" valign="top" >
<code><span id="TYPEELTt.target_variance">target_variance</span>&nbsp;: <code class="type">float</code>;</code></td>
<td class="typefieldcomment" align="left" valign="top" ><code>(*</code></td><td class="typefieldcomment" align="left" valign="top" ><div class="info ">
Variance of targets<br>
</div>
</td><td class="typefieldcomment" align="left" valign="bottom" ><code>*)</code></td>
</tr>
<tr>
<td align="left" valign="top" >
<code>&nbsp;&nbsp;</code></td>
<td align="left" valign="top" >
<code><span id="TYPEELTt.sse">sse</span>&nbsp;: <code class="type">float</code>;</code></td>
<td class="typefieldcomment" align="left" valign="top" ><code>(*</code></td><td class="typefieldcomment" align="left" valign="top" ><div class="info ">
Sum of squared errors<br>
</div>
</td><td class="typefieldcomment" align="left" valign="bottom" ><code>*)</code></td>
</tr>
<tr>
<td align="left" valign="top" >
<code>&nbsp;&nbsp;</code></td>
<td align="left" valign="top" >
<code><span id="TYPEELTt.mse">mse</span>&nbsp;: <code class="type">float</code>;</code></td>
<td class="typefieldcomment" align="left" valign="top" ><code>(*</code></td><td class="typefieldcomment" align="left" valign="top" ><div class="info ">
Mean sum of squared errors<br>
</div>
</td><td class="typefieldcomment" align="left" valign="bottom" ><code>*)</code></td>
</tr>
<tr>
<td align="left" valign="top" >
<code>&nbsp;&nbsp;</code></td>
<td align="left" valign="top" >
<code><span id="TYPEELTt.rmse">rmse</span>&nbsp;: <code class="type">float</code>;</code></td>
<td class="typefieldcomment" align="left" valign="top" ><code>(*</code></td><td class="typefieldcomment" align="left" valign="top" ><div class="info ">
Root mean sum of squared errors<br>
</div>
</td><td class="typefieldcomment" align="left" valign="bottom" ><code>*)</code></td>
</tr>
<tr>
<td align="left" valign="top" >
<code>&nbsp;&nbsp;</code></td>
<td align="left" valign="top" >
<code><span id="TYPEELTt.smse">smse</span>&nbsp;: <code class="type">float</code>;</code></td>
<td class="typefieldcomment" align="left" valign="top" ><code>(*</code></td><td class="typefieldcomment" align="left" valign="top" ><div class="info ">
Standardized mean squared error<br>
</div>
</td><td class="typefieldcomment" align="left" valign="bottom" ><code>*)</code></td>
</tr>
<tr>
<td align="left" valign="top" >
<code>&nbsp;&nbsp;</code></td>
<td align="left" valign="top" >
<code><span id="TYPEELTt.msll">msll</span>&nbsp;: <code class="type">float</code>;</code></td>
<td class="typefieldcomment" align="left" valign="top" ><code>(*</code></td><td class="typefieldcomment" align="left" valign="top" ><div class="info ">
Mean standardized log loss<br>
</div>
</td><td class="typefieldcomment" align="left" valign="bottom" ><code>*)</code></td>
</tr>
<tr>
<td align="left" valign="top" >
<code>&nbsp;&nbsp;</code></td>
<td align="left" valign="top" >
<code><span id="TYPEELTt.mad">mad</span>&nbsp;: <code class="type">float</code>;</code></td>
<td class="typefieldcomment" align="left" valign="top" ><code>(*</code></td><td class="typefieldcomment" align="left" valign="top" ><div class="info ">
Mean absolute deviation<br>
</div>
</td><td class="typefieldcomment" align="left" valign="bottom" ><code>*)</code></td>
</tr>
<tr>
<td align="left" valign="top" >
<code>&nbsp;&nbsp;</code></td>
<td align="left" valign="top" >
<code><span id="TYPEELTt.maxad">maxad</span>&nbsp;: <code class="type">float</code>;</code></td>
<td class="typefieldcomment" align="left" valign="top" ><code>(*</code></td><td class="typefieldcomment" align="left" valign="top" ><div class="info ">
Maximum absolute deviation<br>
</div>
</td><td class="typefieldcomment" align="left" valign="bottom" ><code>*)</code></td>
</tr></table>
}

<div class="info ">
Type of full statistics<br>
</div>


<pre><span id="VALcalc_n_samples"><span class="keyword">val</span> calc_n_samples</span> : <code class="type"><a href="Gpr_interfaces.Sigs.Eval.Trained.html#TYPEt">Gpr_interfaces.Sigs.Eval.Trained.t</a> -> int</code></pre><div class="info ">
<code class="code">calc_n_samples trained</code><br>
<b>Returns</b> number of samples used for training
          <code class="code">trained</code>.<br>
</div>

<pre><span id="VALcalc_target_variance"><span class="keyword">val</span> calc_target_variance</span> : <code class="type"><a href="Gpr_interfaces.Sigs.Eval.Trained.html#TYPEt">Gpr_interfaces.Sigs.Eval.Trained.t</a> -> float</code></pre><div class="info ">
<code class="code">calc_target_variance trained</code><br>
<b>Returns</b> variance of targets used for
          training <code class="code">trained</code>.<br>
</div>

<pre><span id="VALcalc_sse"><span class="keyword">val</span> calc_sse</span> : <code class="type"><a href="Gpr_interfaces.Sigs.Eval.Trained.html#TYPEt">Gpr_interfaces.Sigs.Eval.Trained.t</a> -> float</code></pre><div class="info ">
<code class="code">calc_sse trained</code><br>
<b>Returns</b> the sum of squared errors of the <code class="code">trained</code>
          model.<br>
</div>

<pre><span id="VALcalc_mse"><span class="keyword">val</span> calc_mse</span> : <code class="type"><a href="Gpr_interfaces.Sigs.Eval.Trained.html#TYPEt">Gpr_interfaces.Sigs.Eval.Trained.t</a> -> float</code></pre><div class="info ">
<code class="code">calc_mse trained</code><br>
<b>Returns</b> the mean sum of squared errors of the
          <code class="code">trained</code> model.<br>
</div>

<pre><span id="VALcalc_rmse"><span class="keyword">val</span> calc_rmse</span> : <code class="type"><a href="Gpr_interfaces.Sigs.Eval.Trained.html#TYPEt">Gpr_interfaces.Sigs.Eval.Trained.t</a> -> float</code></pre><div class="info ">
<code class="code">calc_sse trained</code><br>
<b>Returns</b> the root of the mean sum of squared errors
          of the <code class="code">trained</code> model.<br>
</div>

<pre><span id="VALcalc_smse"><span class="keyword">val</span> calc_smse</span> : <code class="type"><a href="Gpr_interfaces.Sigs.Eval.Trained.html#TYPEt">Gpr_interfaces.Sigs.Eval.Trained.t</a> -> float</code></pre><div class="info ">
<code class="code">calc_smse trained</code><br>
<b>Returns</b> the standardized mean squared error of the
          <code class="code">trained</code> model.  This is equivalent to the mean squared error divided
          by the target variance.<br>
</div>

<pre><span id="VALcalc_msll"><span class="keyword">val</span> calc_msll</span> : <code class="type"><a href="Gpr_interfaces.Sigs.Eval.Trained.html#TYPEt">Gpr_interfaces.Sigs.Eval.Trained.t</a> -> float</code></pre><div class="info ">
<code class="code">calc_msll trained</code><br>
<b>Returns</b> the mean standardized log loss.  This
          is equivalent to subtracting the log evidence of the trained model
          from the log evidence of a normal distribution fit to the targets, and
          dividing the result by the number of samples.<br>
</div>

<pre><span id="VALcalc_mad"><span class="keyword">val</span> calc_mad</span> : <code class="type"><a href="Gpr_interfaces.Sigs.Eval.Trained.html#TYPEt">Gpr_interfaces.Sigs.Eval.Trained.t</a> -> float</code></pre><div class="info ">
<code class="code">calc_mad trained</code><br>
<b>Returns</b> the mean absolute deviation
          of the <code class="code">trained</code> model.<br>
</div>

<pre><span id="VALcalc_maxad"><span class="keyword">val</span> calc_maxad</span> : <code class="type"><a href="Gpr_interfaces.Sigs.Eval.Trained.html#TYPEt">Gpr_interfaces.Sigs.Eval.Trained.t</a> -> float</code></pre><div class="info ">
<code class="code">calc_mad trained</code><br>
<b>Returns</b> the maximum absolute deviation
          of the <code class="code">trained</code> model.<br>
</div>

<pre><span id="VALcalc"><span class="keyword">val</span> calc</span> : <code class="type"><a href="Gpr_interfaces.Sigs.Eval.Trained.html#TYPEt">Gpr_interfaces.Sigs.Eval.Trained.t</a> -> <a href="Gpr_interfaces.Sigs.Eval.Stats.html#TYPEt">t</a></code></pre><div class="info ">
<code class="code">calc trained</code><br>
<b>Returns</b> the full set of statistics associated with
          the <code class="code">trained</code> model.<br>
</div>
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