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<li><a class="reference internal" href="#">Column Transformer with Heterogeneous Data Sources</a><ul>
<li><a class="reference internal" href="#newsgroups-dataset">20 newsgroups dataset</a></li>
<li><a class="reference internal" href="#creating-transformers">Creating transformers</a></li>
<li><a class="reference internal" href="#classification-pipeline">Classification pipeline</a></li>
</ul>
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<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-compose-plot-column-transformer-py"><span class="std std-ref">here</span></a>
to download the full example code or to run this example in your browser via Binder</p>
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<section class="sphx-glr-example-title" id="column-transformer-with-heterogeneous-data-sources">
<span id="sphx-glr-auto-examples-compose-plot-column-transformer-py"></span><h1>Column Transformer with Heterogeneous Data Sources<a class="headerlink" href="#column-transformer-with-heterogeneous-data-sources" title="Permalink to this heading">¶</a></h1>
<p>Datasets can often contain components that require different feature
extraction and processing pipelines. This scenario might occur when:</p>
<ol class="arabic simple">
<li><p>your dataset consists of heterogeneous data types (e.g. raster images and
text captions),</p></li>
<li><p>your dataset is stored in a <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a> and different columns
require different processing pipelines.</p></li>
</ol>
<p>This example demonstrates how to use
<a class="reference internal" href="../../modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">ColumnTransformer</span></code></a> on a dataset containing
different types of features. The choice of features is not particularly
helpful, but serves to illustrate the technique.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Matt Terry <[email protected]></span>
<span class="c1">#</span>
<span class="c1"># License: BSD 3 clause</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer" title="sklearn.preprocessing.FunctionTransformer" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">FunctionTransformer</span></a>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.fetch_20newsgroups.html#sklearn.datasets.fetch_20newsgroups" title="sklearn.datasets.fetch_20newsgroups" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">fetch_20newsgroups</span></a>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.decomposition.TruncatedSVD.html#sklearn.decomposition.TruncatedSVD" title="sklearn.decomposition.TruncatedSVD" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TruncatedSVD</span></a>
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.feature_extraction.DictVectorizer.html#sklearn.feature_extraction.DictVectorizer" title="sklearn.feature_extraction.DictVectorizer" class="sphx-glr-backref-module-sklearn-feature_extraction sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DictVectorizer</span></a>
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer" title="sklearn.feature_extraction.text.TfidfVectorizer" class="sphx-glr-backref-module-sklearn-feature_extraction-text sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TfidfVectorizer</span></a>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Pipeline</span></a>
<span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer" class="sphx-glr-backref-module-sklearn-compose sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ColumnTransformer</span></a>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LinearSVC</span></a>
</pre></div>
</div>
<section id="newsgroups-dataset">
<h2>20 newsgroups dataset<a class="headerlink" href="#newsgroups-dataset" title="Permalink to this heading">¶</a></h2>
<p>We will use the <a class="reference internal" href="../../datasets/real_world.html#newsgroups-dataset"><span class="std std-ref">20 newsgroups dataset</span></a>, which
comprises posts from newsgroups on 20 topics. This dataset is split
into train and test subsets based on messages posted before and after
a specific date. We will only use posts from 2 categories to speed up running
time.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">categories</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"sci.med"</span><span class="p">,</span> <span class="s2">"sci.space"</span><span class="p">]</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.fetch_20newsgroups.html#sklearn.datasets.fetch_20newsgroups" title="sklearn.datasets.fetch_20newsgroups" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">fetch_20newsgroups</span></a><span class="p">(</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">subset</span><span class="o">=</span><span class="s2">"train"</span><span class="p">,</span>
<span class="n">categories</span><span class="o">=</span><span class="n">categories</span><span class="p">,</span>
<span class="n">remove</span><span class="o">=</span><span class="p">(</span><span class="s2">"footers"</span><span class="p">,</span> <span class="s2">"quotes"</span><span class="p">),</span>
<span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.fetch_20newsgroups.html#sklearn.datasets.fetch_20newsgroups" title="sklearn.datasets.fetch_20newsgroups" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">fetch_20newsgroups</span></a><span class="p">(</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">subset</span><span class="o">=</span><span class="s2">"test"</span><span class="p">,</span>
<span class="n">categories</span><span class="o">=</span><span class="n">categories</span><span class="p">,</span>
<span class="n">remove</span><span class="o">=</span><span class="p">(</span><span class="s2">"footers"</span><span class="p">,</span> <span class="s2">"quotes"</span><span class="p">),</span>
<span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Each feature comprises meta information about that post, such as the subject,
and the body of the news post.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>From: [email protected] (fred j mccall 575-3539)
Subject: Re: Metric vs English
Article-I.D.: mksol.1993Apr6.131900.8407
Organization: Texas Instruments Inc
Lines: 31
American, perhaps, but nothing military about it. I learned (mostly)
slugs when we talked English units in high school physics and while
the teacher was an ex-Navy fighter jock the book certainly wasn't
produced by the military.
[Poundals were just too flinking small and made the math come out
funny; sort of the same reason proponents of SI give for using that.]
--
"Insisting on perfect safety is for people who don't have the balls to live
in the real world." -- Mary Shafer, NASA Ames Dryden
</pre></div>
</div>
</section>
<section id="creating-transformers">
<h2>Creating transformers<a class="headerlink" href="#creating-transformers" title="Permalink to this heading">¶</a></h2>
<p>First, we would like a transformer that extracts the subject and
body of each post. Since this is a stateless transformation (does not
require state information from training data), we can define a function that
performs the data transformation then use
<a class="reference internal" href="../../modules/generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer" title="sklearn.preprocessing.FunctionTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">FunctionTransformer</span></code></a> to create a scikit-learn
transformer.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">subject_body_extractor</span><span class="p">(</span><span class="n">posts</span><span class="p">):</span>
<span class="c1"># construct object dtype array with two columns</span>
<span class="c1"># first column = 'subject' and second column = 'body'</span>
<span class="n">features</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.empty.html#numpy.empty" title="numpy.empty" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">empty</span></a><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">posts</span><span class="p">),</span> <span class="mi">2</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">text</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">posts</span><span class="p">):</span>
<span class="c1"># temporary variable `_` stores '\n\n'</span>
<span class="n">headers</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">body</span> <span class="o">=</span> <span class="n">text</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s2">"</span><span class="se">\n\n</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># store body text in second column</span>
<span class="n">features</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">body</span>
<span class="n">prefix</span> <span class="o">=</span> <span class="s2">"Subject:"</span>
<span class="n">sub</span> <span class="o">=</span> <span class="s2">""</span>
<span class="c1"># save text after 'Subject:' in first column</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">headers</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="p">):</span>
<span class="k">if</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="n">prefix</span><span class="p">):</span>
<span class="n">sub</span> <span class="o">=</span> <span class="n">line</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">prefix</span><span class="p">)</span> <span class="p">:]</span>
<span class="k">break</span>
<span class="n">features</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">sub</span>
<span class="k">return</span> <span class="n">features</span>
<span class="n">subject_body_transformer</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer" title="sklearn.preprocessing.FunctionTransformer" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">FunctionTransformer</span></a><span class="p">(</span><span class="n">subject_body_extractor</span><span class="p">)</span>
</pre></div>
</div>
<p>We will also create a transformer that extracts the
length of the text and the number of sentences.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">text_stats</span><span class="p">(</span><span class="n">posts</span><span class="p">):</span>
<span class="k">return</span> <span class="p">[{</span><span class="s2">"length"</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">text</span><span class="p">),</span> <span class="s2">"num_sentences"</span><span class="p">:</span> <span class="n">text</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s2">"."</span><span class="p">)}</span> <span class="k">for</span> <span class="n">text</span> <span class="ow">in</span> <span class="n">posts</span><span class="p">]</span>
<span class="n">text_stats_transformer</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer" title="sklearn.preprocessing.FunctionTransformer" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">FunctionTransformer</span></a><span class="p">(</span><span class="n">text_stats</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="classification-pipeline">
<h2>Classification pipeline<a class="headerlink" href="#classification-pipeline" title="Permalink to this heading">¶</a></h2>
<p>The pipeline below extracts the subject and body from each post using
<code class="docutils literal notranslate"><span class="pre">SubjectBodyExtractor</span></code>, producing a (n_samples, 2) array. This array is
then used to compute standard bag-of-words features for the subject and body
as well as text length and number of sentences on the body, using
<code class="docutils literal notranslate"><span class="pre">ColumnTransformer</span></code>. We combine them, with weights, then train a
classifier on the combined set of features.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pipeline</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Pipeline</span></a><span class="p">(</span>
<span class="p">[</span>
<span class="c1"># Extract subject & body</span>
<span class="p">(</span><span class="s2">"subjectbody"</span><span class="p">,</span> <span class="n">subject_body_transformer</span><span class="p">),</span>
<span class="c1"># Use ColumnTransformer to combine the subject and body features</span>
<span class="p">(</span>
<span class="s2">"union"</span><span class="p">,</span>
<a href="../../modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer" class="sphx-glr-backref-module-sklearn-compose sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ColumnTransformer</span></a><span class="p">(</span>
<span class="p">[</span>
<span class="c1"># bag-of-words for subject (col 0)</span>
<span class="p">(</span><span class="s2">"subject"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer" title="sklearn.feature_extraction.text.TfidfVectorizer" class="sphx-glr-backref-module-sklearn-feature_extraction-text sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TfidfVectorizer</span></a><span class="p">(</span><span class="n">min_df</span><span class="o">=</span><span class="mi">50</span><span class="p">),</span> <span class="mi">0</span><span class="p">),</span>
<span class="c1"># bag-of-words with decomposition for body (col 1)</span>
<span class="p">(</span>
<span class="s2">"body_bow"</span><span class="p">,</span>
<a href="../../modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Pipeline</span></a><span class="p">(</span>
<span class="p">[</span>
<span class="p">(</span><span class="s2">"tfidf"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer" title="sklearn.feature_extraction.text.TfidfVectorizer" class="sphx-glr-backref-module-sklearn-feature_extraction-text sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TfidfVectorizer</span></a><span class="p">()),</span>
<span class="p">(</span><span class="s2">"best"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.decomposition.TruncatedSVD.html#sklearn.decomposition.TruncatedSVD" title="sklearn.decomposition.TruncatedSVD" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TruncatedSVD</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">50</span><span class="p">)),</span>
<span class="p">]</span>
<span class="p">),</span>
<span class="mi">1</span><span class="p">,</span>
<span class="p">),</span>
<span class="c1"># Pipeline for pulling text stats from post's body</span>
<span class="p">(</span>
<span class="s2">"body_stats"</span><span class="p">,</span>
<a href="../../modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Pipeline</span></a><span class="p">(</span>
<span class="p">[</span>
<span class="p">(</span>
<span class="s2">"stats"</span><span class="p">,</span>
<span class="n">text_stats_transformer</span><span class="p">,</span>
<span class="p">),</span> <span class="c1"># returns a list of dicts</span>
<span class="p">(</span>
<span class="s2">"vect"</span><span class="p">,</span>
<a href="../../modules/generated/sklearn.feature_extraction.DictVectorizer.html#sklearn.feature_extraction.DictVectorizer" title="sklearn.feature_extraction.DictVectorizer" class="sphx-glr-backref-module-sklearn-feature_extraction sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DictVectorizer</span></a><span class="p">(),</span>
<span class="p">),</span> <span class="c1"># list of dicts -> feature matrix</span>
<span class="p">]</span>
<span class="p">),</span>
<span class="mi">1</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">],</span>
<span class="c1"># weight above ColumnTransformer features</span>
<span class="n">transformer_weights</span><span class="o">=</span><span class="p">{</span>
<span class="s2">"subject"</span><span class="p">:</span> <span class="mf">0.8</span><span class="p">,</span>
<span class="s2">"body_bow"</span><span class="p">:</span> <span class="mf">0.5</span><span class="p">,</span>
<span class="s2">"body_stats"</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span>
<span class="p">},</span>
<span class="p">),</span>
<span class="p">),</span>
<span class="c1"># Use a SVC classifier on the combined features</span>
<span class="p">(</span><span class="s2">"svc"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LinearSVC</span></a><span class="p">(</span><span class="n">dual</span><span class="o">=</span><span class="kc">False</span><span class="p">)),</span>
<span class="p">],</span>
<span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Finally, we fit our pipeline on the training data and use it to predict
topics for <code class="docutils literal notranslate"><span class="pre">X_test</span></code>. Performance metrics of our pipeline are then printed.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Classification report:</span><span class="se">\n\n</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><a href="../../modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)))</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[Pipeline] ....... (step 1 of 3) Processing subjectbody, total= 0.0s
[Pipeline] ............. (step 2 of 3) Processing union, total= 0.4s
[Pipeline] ............... (step 3 of 3) Processing svc, total= 0.0s
Classification report:
precision recall f1-score support
0 0.84 0.87 0.86 396
1 0.87 0.83 0.85 394
accuracy 0.85 790
macro avg 0.85 0.85 0.85 790
weighted avg 0.85 0.85 0.85 790
</pre></div>
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