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{% extends 'base.html' %}
{% block title %}q2-sample-classifier : {{ title }}{% endblock %}
{% block fixed %}{% endblock %}
{% block content %}
{% if warning_msg %}
<div class="panel-group" id="warnings" role="tablist" aria-multiselectable="true">
<div class="panel panel-warning">
<div class="panel-heading" role="tab" id="warnings-heading">
<h4 class="panel-title">
<a role="button" data-toggle="collapse" data-parent="#warnings" href="#warnings-list" aria-expanded="true" aria-controls="warnings-list">
Warnings (click here to collapse/expand):
</a>
</h4>
</div>
<div id="warnings-list" class="panel-collapse collapse in" role="tabpanel" aria-labelledby="warnings-heading">
<div class="alert alert-warning col-md-12">
<p><strong>{{ warning_msg }}</strong></p>
</div>
</div>
</div>
</div>
{% endif %}
<div class="row">
{% if predictions %}
<h1>Model Accuracy</h1>
{% endif %}
<div class="text-center">
{% if predictions %}
<a href="predictions.pdf">
<img src="predictions.png">
<br>
<p>Download as PDF</p>
</a>
{% endif %}
{% if predictions %}
<div class="col-lg-12">
{{ predictions }}
<a href="predictive_accuracy.tsv">
<p>Download accuracy results as tsv</p>
</a>
</div>
{% endif %}
{% if roc %}
<div class="col-lg-12">
<h1>Receiver Operating Characteristic Curves</h1>
<a href="roc_plot.pdf">
<img src="roc_plot.png">
<br>
<p>Download as PDF</p>
</a>
<div class="text-justify">
<p>Receiver Operating Characteristic (ROC) curves are a graphical
representation of the classification accuracy of a machine-learning
model. The ROC curve plots the relationship between the true positive
rate (TPR, on the y-axis) and the false positive rate (FPR, on the
x-axis) at various threshold settings. Thus, the top-left corner of the
plot represents the "optimal" performance position, indicating a FPR
of zero and a TPR of one. This "optimal" scenario is unlikely to occur
in practice, but a greater area under the curve (AUC) indicates better
performance. This can be compared to the error rate achieved by random
chance, which is represented here as a diagonal line extending from the
lower-left to upper-right corners. Additionally, the "steepness" of the
curve is important, as a good classifier should maximize the TPR while
minimizing the FPR.
In addition to showing the ROC curves for each class, average ROCs and
AUCs are calculated. "Micro-averaging" calculates metrics globally by
averaging across each sample; hence class imbalance impacts this metric.
"Macro-averaging" is another average metric, which gives equal weight to
the classification of each sample.</p>
</div>
</div>
{% endif %}
{% if optimize_feature_selection %}
<h1>Recursive feature extraction</h1>
<div class="text-center">
<a href="rfe_plot.pdf">
<img src="rfe_plot.png">
<br>
<p>Download as PDF</p>
</a>
<a href="rfe_scores.tsv">
<p>Download as TSV</p>
</a>
</div>
{% endif %}
{% if result %}
<h1>Model parameters</h1>
<div class="col-lg-12">
{{ result }}
</div>
{% endif %}
</div>
</div>
{% endblock %}
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