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18 changes: 9 additions & 9 deletions _notebooks/Basic_usage.html
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Expand Up @@ -241,7 +241,7 @@ <h1>🚩 Basic usage<a class="headerlink" href="#basic-usage" title="Permalink t
</div>
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<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;0.4.0rc2&#39;
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Expand Down Expand Up @@ -547,7 +547,7 @@ <h2>Imbalance metrics<a class="headerlink" href="#imbalance-metrics" title="Perm
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<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.lines.Line2D at 0x7f9ed45c90d0&gt;
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<img alt="../_images/2bb44d03d42247969124582eea41e70bf462d350c4bfaceb853eecf4e89f5d6d.png" src="../_images/2bb44d03d42247969124582eea41e70bf462d350c4bfaceb853eecf4e89f5d6d.png" />
Expand Down Expand Up @@ -630,7 +630,7 @@ <h2>Outliers<a class="headerlink" href="#outliers" title="Permalink to this head
use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
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Expand Down Expand Up @@ -689,10 +689,10 @@ <h2>Outliers<a class="headerlink" href="#outliers" title="Permalink to this head
use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
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<p>This truncated normal distribution has no outliers (there are only about 60, compared to the 100 we expect at this confidence level of 99% on this dataset of 10,000 records).</p>
Expand Down Expand Up @@ -737,7 +737,7 @@ <h2>Clipping<a class="headerlink" href="#clipping" title="Permalink to this head
use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
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Expand Down Expand Up @@ -798,7 +798,7 @@ <h2>Distribution shape<a class="headerlink" href="#distribution-shape" title="Pe
use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
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<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([0.10111405, 0.3631455 , 0.50857281, 0.02716764, 0. ])
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18 changes: 10 additions & 8 deletions _notebooks/Tutorial.html
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Expand Up @@ -318,7 +318,7 @@ <h2>A quick look at <code class="docutils literal notranslate"><span class="pre"
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<div class="cell_output docutils container">
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;0.4.0rc2&#39;
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Expand Down Expand Up @@ -570,7 +570,7 @@ <h2>Clipping<a class="headerlink" href="#clipping" title="Permalink to this head
use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
</pre></div>
</div>
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.FacetGrid at 0x7fdb872d1950&gt;
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Expand Down Expand Up @@ -623,7 +623,7 @@ <h2>Importance<a class="headerlink" href="#importance" title="Permalink to this
</div>
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<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([0.4344031 , 0.15806467, 0.31618159, 0.09135064])
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>🚩 The labels are imbalanced by more than the threshold (0.420 &gt; 0.400). See self.minority_classes_ for the minority classes.
🚩 Features 0, 1 have samples that may be clipped.
🚩 Features 0, 1, 2 have samples that may be correlated.
🚩 Feature 0 has a multimodal distribution.
</pre></div>
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>🚩 Feature 0 has a multimodal distribution.
ℹ️ Multimodality detection may not have succeeded for all groups in all features.
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>🚩 There are more outliers than expected in the training data (316 vs 31).
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>🚩 Feature 3 has low importance; check for relevance.
ℹ️ Dummy classifier scores: {&#39;f1&#39;: 0.2721679798858705, &#39;roc_auc&#39;: 0.5022770183234067} (stratified strategy).
ℹ️ Dummy classifier scores: {&#39;f1&#39;: 0.25488459423559595, &#39;roc_auc&#39;: 0.5} (most_frequent strategy).
</pre></div>
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Detector(func=&lt;function BaseRedflagDetector.__init__.&lt;locals&gt;.&lt;lambda&gt; at 0x7fdb84f9bd80&gt;,
Detector(func=&lt;function BaseRedflagDetector.__init__.&lt;locals&gt;.&lt;lambda&gt; at 0x7f55b5bd7c40&gt;,
message=&#x27;are negative&#x27;)),
(&#x27;svc&#x27;, SVC())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-34" type="checkbox" ><label for="sk-estimator-id-34" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;detector&#x27;,
Detector(func=&lt;function BaseRedflagDetector.__init__.&lt;locals&gt;.&lt;lambda&gt; at 0x7fdb84f9bd80&gt;,
Detector(func=&lt;function BaseRedflagDetector.__init__.&lt;locals&gt;.&lt;lambda&gt; at 0x7f55b5bd7c40&gt;,
message=&#x27;are negative&#x27;)),
(&#x27;svc&#x27;, SVC())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-35" type="checkbox" ><label for="sk-estimator-id-35" class="sk-toggleable__label sk-toggleable__label-arrow">Detector</label><div class="sk-toggleable__content"><pre>Detector(func=&lt;function BaseRedflagDetector.__init__.&lt;locals&gt;.&lt;lambda&gt; at 0x7fdb84f9bd80&gt;,
(&#x27;svc&#x27;, SVC())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-35" type="checkbox" ><label for="sk-estimator-id-35" class="sk-toggleable__label sk-toggleable__label-arrow">Detector</label><div class="sk-toggleable__content"><pre>Detector(func=&lt;function BaseRedflagDetector.__init__.&lt;locals&gt;.&lt;lambda&gt; at 0x7f55b5bd7c40&gt;,
message=&#x27;are negative&#x27;)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-36" type="checkbox" ><label for="sk-estimator-id-36" class="sk-toggleable__label sk-toggleable__label-arrow">SVC</label><div class="sk-toggleable__content"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div>
</div>
<p>The noise feature we added has negative values; the others are all positive, which is what we expect for these data.</p>
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