<p><div><div><div><div data-width="100" data-show-faces="false" data-send="true" data-layout="button_count" data-height="21" data-href="https://www.r-bloggers.com/data-science-live-book-scoring-model-performance-profiling-update/"></div></div><div><a data-url="https://www.r-bloggers.com/data-science-live-book-scoring-model-performance-profiling-update/" data-counturl="https://www.r-bloggers.com/data-science-live-book-scoring-model-performance-profiling-update/" data-count="horizontal" data-text="Data Science Live Book – Scoring, Model Performance &amp; profiling – Update!" class="twitter-share-button" data-via="rbloggers" href="https://twitter.com/share"></a></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="http://blog.datascienceheroes.com/data-science-live-book-scoring-model-performance-profiling-update/"> R - Data Science Heroes Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><img alt="Data Science Live Book - Scoring, Model Performance &amp; profiling - Update!" src="https://i1.wp.com/blog.datascienceheroes.com/content/images/2016/10/cover.png?w=456" data-recalc-dims="1">
<p><img alt="Data Science Live Book - Scoring, Model Performance &amp; profiling - Update!" src="https://i0.wp.com/datascienceheroes.com/img/blog/cover.png?fit=456%2C270" data-recalc-dims="1"></p>
<p>This update contains a new chapter –<strong>scoring</strong>– which is related to <strong>model performance</strong> and <strong>model deployment</strong>, used when predicting a binary outcome. </p>
<p><a target="_blank" href="http://livebook.datascienceheroes.com/scoring/scoring.html">Link to the scoring chapter</a>.</p>
<p><img alt="Data Science Live Book - Scoring, Model Performance &amp; profiling - Update!" src="https://i1.wp.com/datascienceheroes.com/img/blog/scoring.png?fit=456%2C400" data-recalc-dims="1"></p>
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<p><strong>Important</strong>: To use following updates please update funModeling package <img alt="🙂" class="wp-smiley" src="https://s.w.org/images/core/emoji/2/72x72/1f642.png"></p>
<p><code>install.packages(&quot;funModeling&quot;)</code> </p>
<p>Also related to predictive modelling for binary outcome, there is a new chapter based on how to compare models using the <strong>gain</strong> and <strong>lift charts</strong>. <br>
<a target="_blank" href="http://livebook.datascienceheroes.com/model_performance/gain_lift.html">Link to the gain and lift chapter</a>.</p>
<p><img alt="Data Science Live Book - Scoring, Model Performance &amp; profiling - Update!" src="https://i0.wp.com/datascienceheroes.com/img/blog/lift_gain.png?fit=456%2C200" data-recalc-dims="1"></p>
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<p>Finally there is a new function, <code>freq</code>, which generates the common <strong>frequency analysis</strong> plus the table with the numbers. <br>
<em>This function can runs automatically for all the data input, and export all the images at once</em>.</p>
<p><a target="_blank" href="http://livebook.datascienceheroes.com/data_preparation/profiling.html">Link to the frequency function</a> It’s at the bottom of the page.</p>
<p><img alt="Data Science Live Book - Scoring, Model Performance &amp; profiling - Update!" src="https://i0.wp.com/datascienceheroes.com/img/blog/frequency_ananalysis_2.png?fit=456%2C400" data-recalc-dims="1"></p>
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<p><font size="3"><em>First published at: <a target="_blank" href="http://blog.datascienceheroes.com/data-science-live-book-scoring-model-performance-profiling-update">http://blog.datascienceheroes.com/data-science-live-book-scoring-model-performance-profiling-update</a></em></font></p>
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