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<div  id='write'  class = 'is-node show-fences-line-number'><div class='md-toc' mdtype='toc'><p class="md-toc-content"><span class="md-toc-item md-toc-h1" data-ref="n2"><a class="md-toc-inner" href="#header-n2">10 Advice for Applying Machine Learning</a></span><span class="md-toc-item md-toc-h2" data-ref="n3"><a class="md-toc-inner" href="#header-n3">10.1 Deciding What to Try Next</a></span><span class="md-toc-item md-toc-h2" data-ref="n4"><a class="md-toc-inner" href="#header-n4">10.2 Evaluating a Hypothesis</a></span><span class="md-toc-item md-toc-h2" data-ref="n5"><a class="md-toc-inner" href="#header-n5">10.3 Model Selection and Train/Validation/Test Sets</a></span><span class="md-toc-item md-toc-h2" data-ref="n6"><a class="md-toc-inner" href="#header-n6">10.4 Diagnosing Bias vs. Variance</a></span><span class="md-toc-item md-toc-h2" data-ref="n7"><a class="md-toc-inner" href="#header-n7">10.5 Regularization and Bias/Variance</a></span><span class="md-toc-item md-toc-h2" data-ref="n8"><a class="md-toc-inner" href="#header-n8">10.6 Learning Curves</a></span><span class="md-toc-item md-toc-h2" data-ref="n9"><a class="md-toc-inner" href="#header-n9">10.7 Deciding What to Do Next Revisited</a></span><span class="md-toc-item md-toc-h1" data-ref="n12"><a class="md-toc-inner" href="#header-n12">11 Machine Learning System Design</a></span><span class="md-toc-item md-toc-h2" data-ref="n13"><a class="md-toc-inner" href="#header-n13">11.1 Prioritizing What to Work On</a></span><span class="md-toc-item md-toc-h2" data-ref="n14"><a class="md-toc-inner" href="#header-n14">11.2 Error Analysis</a></span><span class="md-toc-item md-toc-h2" data-ref="n15"><a class="md-toc-inner" href="#header-n15">11.3 Error Metrics for Skewed Classes</a></span><span class="md-toc-item md-toc-h2" data-ref="n16"><a class="md-toc-inner" href="#header-n16">11.4 Trading Off Precision and Recall</a></span><span class="md-toc-item md-toc-h2" data-ref="n17"><a class="md-toc-inner" href="#header-n17">11.5 Data For Machine Learning</a></span></p></div><h1><a name='header-n2' class='md-header-anchor '></a>10 Advice for Applying Machine Learning</h1><h2><a name='header-n3' class='md-header-anchor '></a>10.1 Deciding What to Try Next</h2><h2><a name='header-n4' class='md-header-anchor '></a>10.2 Evaluating a Hypothesis</h2><h2><a name='header-n5' class='md-header-anchor '></a>10.3 Model Selection and Train/Validation/Test Sets</h2><h2><a name='header-n6' class='md-header-anchor '></a>10.4 Diagnosing Bias vs. Variance</h2><h2><a name='header-n7' class='md-header-anchor '></a>10.5 Regularization and Bias/Variance</h2><h2><a name='header-n8' class='md-header-anchor '></a>10.6 Learning Curves</h2><h2><a name='header-n9' class='md-header-anchor '></a>10.7 Deciding What to Do Next Revisited</h2><p> </p><h1><a name='header-n12' class='md-header-anchor '></a>11 Machine Learning System Design</h1><h2><a name='header-n13' class='md-header-anchor '></a>11.1 Prioritizing What to Work On</h2><h2><a name='header-n14' class='md-header-anchor '></a>11.2 Error Analysis</h2><h2><a name='header-n15' class='md-header-anchor '></a>11.3 Error Metrics for Skewed Classes</h2><h2><a name='header-n16' class='md-header-anchor '></a>11.4 Trading Off Precision and Recall</h2><h2><a name='header-n17' class='md-header-anchor '></a>11.5 Data For Machine Learning</h2></div>
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