Software Open Access

rasbt/mlxtend: Version 0.19.0

Sebastian Raschka; James Bourbeau; Maitreyee Mhasakar; Reiichiro Nakano; Kota Mori; Zach Griffith; JJLWHarrison; Jakub Šmíd; Daniel; Francisco J. H. Heras; Guillaume Poirier-Morency; Qiang Gu; Colin; Floris Hoogenboom; Steve Harenberg; Vahid MIRJALILI; Denis Barbier; Marco Tiraboschi; hanzgs; Florian Charlier; Alejandro Correa Bahnsen; Gabriel Azevedo Ferreira; Janpreet Singh; João Pedro Zanlorensi Cardoso; Laurens Geffert; Oliver Tomic; Pablo Fernandez; Christos Aridas; Selay; Ackerley Tng


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  <identifier identifierType="DOI">10.5281/zenodo.5398901</identifier>
  <creators>
    <creator>
      <creatorName>Sebastian Raschka</creatorName>
      <affiliation>UW-Madison</affiliation>
    </creator>
    <creator>
      <creatorName>James Bourbeau</creatorName>
      <affiliation>@coiled</affiliation>
    </creator>
    <creator>
      <creatorName>Maitreyee Mhasakar</creatorName>
    </creator>
    <creator>
      <creatorName>Reiichiro Nakano</creatorName>
      <affiliation>@openai</affiliation>
    </creator>
    <creator>
      <creatorName>Kota Mori</creatorName>
    </creator>
    <creator>
      <creatorName>Zach Griffith</creatorName>
      <affiliation>@WIPACrepo</affiliation>
    </creator>
    <creator>
      <creatorName>JJLWHarrison</creatorName>
    </creator>
    <creator>
      <creatorName>Jakub Šmíd</creatorName>
      <affiliation>Blindspot Solutions</affiliation>
    </creator>
    <creator>
      <creatorName>Daniel</creatorName>
    </creator>
    <creator>
      <creatorName>Francisco J. H. Heras</creatorName>
      <affiliation>Champalimaud Research</affiliation>
    </creator>
    <creator>
      <creatorName>Guillaume Poirier-Morency</creatorName>
      <affiliation>@PavlidisLab at Michael Smith Laboratories</affiliation>
    </creator>
    <creator>
      <creatorName>Qiang Gu</creatorName>
    </creator>
    <creator>
      <creatorName>Colin</creatorName>
      <affiliation>Google</affiliation>
    </creator>
    <creator>
      <creatorName>Floris Hoogenboom</creatorName>
      <affiliation>Royal Schiphol Group</affiliation>
    </creator>
    <creator>
      <creatorName>Steve Harenberg</creatorName>
    </creator>
    <creator>
      <creatorName>Vahid MIRJALILI</creatorName>
      <affiliation>Data Scientist</affiliation>
    </creator>
    <creator>
      <creatorName>Denis Barbier</creatorName>
    </creator>
    <creator>
      <creatorName>Marco Tiraboschi</creatorName>
      <affiliation>University of Milan</affiliation>
    </creator>
    <creator>
      <creatorName>hanzgs</creatorName>
    </creator>
    <creator>
      <creatorName>Florian Charlier</creatorName>
    </creator>
    <creator>
      <creatorName>Alejandro Correa Bahnsen</creatorName>
      <affiliation>Rappi</affiliation>
    </creator>
    <creator>
      <creatorName>Gabriel Azevedo Ferreira</creatorName>
    </creator>
    <creator>
      <creatorName>Janpreet Singh</creatorName>
    </creator>
    <creator>
      <creatorName>João Pedro Zanlorensi Cardoso</creatorName>
      <affiliation>UTFPR</affiliation>
    </creator>
    <creator>
      <creatorName>Laurens Geffert</creatorName>
      <affiliation>Nielsen</affiliation>
    </creator>
    <creator>
      <creatorName>Oliver Tomic</creatorName>
      <affiliation>Norwegian University of Life Sciences</affiliation>
    </creator>
    <creator>
      <creatorName>Pablo Fernandez</creatorName>
      <affiliation>FANSI Motorsport</affiliation>
    </creator>
    <creator>
      <creatorName>Christos Aridas</creatorName>
      <affiliation>Code4Thought</affiliation>
    </creator>
    <creator>
      <creatorName>Selay</creatorName>
    </creator>
    <creator>
      <creatorName>Ackerley Tng</creatorName>
      <affiliation>Centre for Strategic Infocomm Technologies</affiliation>
    </creator>
  </creators>
  <titles>
    <title>rasbt/mlxtend: Version 0.19.0</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-09-02</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5398901</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsSupplementTo">https://github.com/rasbt/mlxtend/tree/v0.19.0</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.594432</relatedIdentifier>
  </relatedIdentifiers>
  <version>v0.19.0</version>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">Version 0.19.0 (09/02/2021)
New Features
&lt;ul&gt;
&lt;li&gt;Adds a second "balanced accuracy" interpretation ("balanced") to &lt;code&gt;evaluate.accuracy_score&lt;/code&gt; in addition to the existing "average" option to compute the scikit-learn-style balanced accuracy. (&lt;a href="https://github.com/rasbt/mlxtend/pull/764"&gt;#764&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;Adds new &lt;code&gt;scatter_hist&lt;/code&gt; function to &lt;code&gt;mlxtend.plotting&lt;/code&gt; for generating a scattered histogram. (&lt;a href="https://github.com/rasbt/mlxtend/issues/757"&gt;#757&lt;/a&gt; via &lt;a href="https://github.com/Maitreyee1"&gt;Maitreyee Mhasaka&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;The &lt;code&gt;evaluate.permutation_test&lt;/code&gt; function now accepts a &lt;code&gt;paired&lt;/code&gt; argument to specify to support paired permutation/randomization tests. (&lt;a href="https://github.com/rasbt/mlxtend/pull/768"&gt;#768&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;The &lt;code&gt;StackingCVRegressor&lt;/code&gt; now also supports multi-dimensional targets similar to &lt;code&gt;StackingRegressor&lt;/code&gt; via &lt;code&gt;StackingCVRegressor(..., multi_output=True)&lt;/code&gt;. (&lt;a href="https://github.com/rasbt/mlxtend/pull/802"&gt;#802&lt;/a&gt; via &lt;a href="ChromaticIsobar"&gt;Marco Tiraboschi&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;
Changes
&lt;ul&gt;
&lt;li&gt;Updates unit tests for scikit-learn 0.24.1 compatibility. (&lt;a href="https://github.com/rasbt/mlxtend/pull/774"&gt;#774&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;code&gt;StackingRegressor&lt;/code&gt; now requires setting &lt;code&gt;StackingRegressor(..., multi_output=True)&lt;/code&gt; if the target is multi-dimensional; this allows for better input validation. (&lt;a href="https://github.com/rasbt/mlxtend/pull/802"&gt;#802&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;Removes deprecated &lt;code&gt;res&lt;/code&gt; argument from &lt;code&gt;plot_decision_regions&lt;/code&gt;. (&lt;a href="https://github.com/rasbt/mlxtend/pull/803"&gt;#803&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;Adds a &lt;code&gt;title_fontsize&lt;/code&gt; parameter to &lt;code&gt;plot_learning_curves&lt;/code&gt; for controlling the title font size; also the plot style is now the matplotlib default. (&lt;a href="https://github.com/rasbt/mlxtend/pull/818"&gt;#818&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;Internal change using &lt;code&gt;'c': 'none'&lt;/code&gt; instead of &lt;code&gt;'c': ''&lt;/code&gt; in &lt;code&gt;mlxtend.plotting.plot_decision_regions&lt;/code&gt;'s scatterplot highlights to stay compatible with Matplotlib 3.4 and newer. (&lt;a href="https://github.com/rasbt/mlxtend/pull/822"&gt;#822&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;Adds a &lt;code&gt;fontcolor_threshold&lt;/code&gt; parameter to the &lt;code&gt;mlxtend.plotting.plot_confusion_matrix&lt;/code&gt; function as an additional option for determining the font color cut-off manually. (&lt;a href="https://github.com/rasbt/mlxtend/pull/827"&gt;#827&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;The &lt;code&gt;frequent_patterns.association_rules&lt;/code&gt; now raises a &lt;code&gt;ValueError&lt;/code&gt; if an empty frequent itemset DataFrame is passed. (&lt;a href="https://github.com/rasbt/mlxtend/pull/843"&gt;#843&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;The .632 and .632+ bootstrap method implemented in the &lt;code&gt;mlxtend.evaluate.bootstrap_point632_score&lt;/code&gt; function now use the whole training set for the resubstitution weighting term instead of the internal training set that is a new bootstrap sample in each round. (&lt;a href="https://github.com/rasbt/mlxtend/pull/844"&gt;#844&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;
Bug Fixes
&lt;ul&gt;
&lt;li&gt;Fixes a typo in the SequentialFeatureSelector documentation (&lt;a href="https://github.com/rasbt/mlxtend/issues/835"&gt;#835&lt;/a&gt; via &lt;a href="https://github.com/joaozanlorensi"&gt;João Pedro Zanlorensi Cardoso&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;</description>
  </descriptions>
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