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update doc
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@aksnzhy
aksnzhy committed Dec 9, 2017
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23 changes: 23 additions & 0 deletions _build/html/_sources/command_line.rst.txt
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Expand Up @@ -104,5 +104,28 @@ machine learning model to solve the binary classification problem: ::
Set Validation Dataset
----------------------------------------

A validation dataset is a set of examples used to tune the hyperparameters of a machine learning model.
In xLearn, users can use ``-v`` option to set the validation data set. For example: ::

./xlearn_train ./small_train.txt -v ./small_test.txt

A portion of xLearn's output: ::

Epoch Train log_loss Test log_loss Time cost (sec)
1 0.575049 0.530560 0.00
2 0.517496 0.537741 0.00
3 0.488428 0.527205 0.00
4 0.469010 0.538175 0.00
5 0.452817 0.537245 0.00
6 0.438929 0.536588 0.00
7 0.423491 0.532349 0.00
8 0.416492 0.541107 0.00
9 0.404554 0.546218 0.00

Here we can see that, the training loss continuously goes down. While, the validation loss (test loss) goes
down first, and then goes up. This is because our model is overfit current training data set.



.. toctree::
:hidden:
20 changes: 20 additions & 0 deletions _build/html/command_line.html
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Expand Up @@ -268,6 +268,26 @@ <h2>Choose Machine Learning Model<a class="headerlink" href="#choose-machine-lea
</div>
<div class="section" id="set-validation-dataset">
<h2>Set Validation Dataset<a class="headerlink" href="#set-validation-dataset" title="Permalink to this headline"></a></h2>
<p>A validation dataset is a set of examples used to tune the hyperparameters of a machine learning model.
In xLearn, users can use <code class="docutils literal"><span class="pre">-v</span></code> option to set the validation data set. For example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">xlearn_train</span> <span class="o">./</span><span class="n">small_train</span><span class="o">.</span><span class="n">txt</span> <span class="o">-</span><span class="n">v</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</span>
</pre></div>
</div>
<p>A portion of xLearn’s output:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">Epoch</span> <span class="n">Train</span> <span class="n">log_loss</span> <span class="n">Test</span> <span class="n">log_loss</span> <span class="n">Time</span> <span class="n">cost</span> <span class="p">(</span><span class="n">sec</span><span class="p">)</span>
<span class="mi">1</span> <span class="mf">0.575049</span> <span class="mf">0.530560</span> <span class="mf">0.00</span>
<span class="mi">2</span> <span class="mf">0.517496</span> <span class="mf">0.537741</span> <span class="mf">0.00</span>
<span class="mi">3</span> <span class="mf">0.488428</span> <span class="mf">0.527205</span> <span class="mf">0.00</span>
<span class="mi">4</span> <span class="mf">0.469010</span> <span class="mf">0.538175</span> <span class="mf">0.00</span>
<span class="mi">5</span> <span class="mf">0.452817</span> <span class="mf">0.537245</span> <span class="mf">0.00</span>
<span class="mi">6</span> <span class="mf">0.438929</span> <span class="mf">0.536588</span> <span class="mf">0.00</span>
<span class="mi">7</span> <span class="mf">0.423491</span> <span class="mf">0.532349</span> <span class="mf">0.00</span>
<span class="mi">8</span> <span class="mf">0.416492</span> <span class="mf">0.541107</span> <span class="mf">0.00</span>
<span class="mi">9</span> <span class="mf">0.404554</span> <span class="mf">0.546218</span> <span class="mf">0.00</span>
</pre></div>
</div>
<p>Here we can see that, the training loss continuously goes down. While, the validation loss (test loss) goes
down first, and then goes up. This is because our model is overfit current training data set.</p>
<blockquote>
<div><div class="toctree-wrapper compound">
</div>
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23 changes: 23 additions & 0 deletions command_line.rst
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Expand Up @@ -104,5 +104,28 @@ machine learning model to solve the binary classification problem: ::
Set Validation Dataset
----------------------------------------

A validation dataset is a set of examples used to tune the hyperparameters of a machine learning model.
In xLearn, users can use ``-v`` option to set the validation data set. For example: ::

./xlearn_train ./small_train.txt -v ./small_test.txt

A portion of xLearn's output: ::

Epoch Train log_loss Test log_loss Time cost (sec)
1 0.575049 0.530560 0.00
2 0.517496 0.537741 0.00
3 0.488428 0.527205 0.00
4 0.469010 0.538175 0.00
5 0.452817 0.537245 0.00
6 0.438929 0.536588 0.00
7 0.423491 0.532349 0.00
8 0.416492 0.541107 0.00
9 0.404554 0.546218 0.00

Here we can see that, the training loss continuously goes down. While, the validation loss (test loss) goes
down first, and then goes up. This is because our model is overfit current training data set.



.. toctree::
:hidden:

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