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(This article was first published on <strong><a href="http://blog.datascienceheroes.com/model-performance-in-data-science-live-book/"> R - Data Science Heroes Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>Hi there! I decided to <em>almost</em> re-write the model validation section since it didn’t reflect real case scenarios. </p>
<p>Hopefully in the two new chapters you will gain a deeper knowledge on methodological aspects on model validation through classical cross-validation, bootstrapping, and going further in the <strong>nature of the error</strong>. And also take advantage of validation when data is <strong>time dependent</strong>.</p>
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<img alt="Model Performance in Data Science Live Book" src="https://i1.wp.com/datascienceheroes.com/img/blog/magnifier.png?w=456" data-recalc-dims="1"> Chapter: Knowing the Error<br>
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<img alt="Model Performance in Data Science Live Book" src="https://i1.wp.com/datascienceheroes.com/img/blog/model_validation_workflow.png?w=456" data-recalc-dims="1"> Chapter: Out-of-Time Validation<br>
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<p>There is a lot more to tell about model validation, but it’s a kick start. </p>
<p>Coming soon, there will be an update on methodological aspects in <strong>data preparation</strong>.</p>
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<p><font size="3"><em>First published at: <a target="_blank" rel="nofollow" href="http://blog.datascienceheroes.com/data-science-live-book-scoring-model-performance-profiling-update">http://blog.datascienceheroes.com/model-performance-in-data-science-live-book</a></em></font></p>
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