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 <h1 class="title toc-ignore">Regime changes</h1>
 <h4 class="author">Francisco Bischoff</h4>
-<h4 class="date">on October 04, 2023</h4>
+<h4 class="date">on November 30, 2023</h4>
 
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@@ -425,7 +425,7 @@ <h4 class="date">on October 04, 2023</h4>
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-<strong>Last updated:</strong> 2023-10-04
+<strong>Last updated:</strong> 2023-11-30
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@@ -553,20 +553,20 @@ <h4 class="date">on October 04, 2023</h4>
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-<a data-toggle="collapse" data-parent="#workflowr-checks" href="#strongRepositoryversionstrongahrefhttpsgithubcomfranzbischofffalsealarmtreed34d2ab00bf3b0588362a0ca9660501e79b786b7targetblankd34d2aba">
+<a data-toggle="collapse" data-parent="#workflowr-checks" href="#strongRepositoryversionstrongahrefhttpsgithubcomfranzbischofffalsealarmtreec772fa09b420cee4c915f894088679aa4c0ea41btargetblankc772fa0a">
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-<strong>Repository version:</strong> <a href="https://github.com/franzbischoff/false.alarm/tree/d34d2ab00bf3b0588362a0ca9660501e79b786b7" target="_blank">d34d2ab</a>
+<strong>Repository version:</strong> <a href="https://github.com/franzbischoff/false.alarm/tree/c772fa09b420cee4c915f894088679aa4c0ea41b" target="_blank">c772fa0</a>
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 Great! You are using Git for version control. Tracking code development and
 connecting the code version to the results is critical for reproducibility.
 </p>
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-The results in this page were generated with repository version <a href="https://github.com/franzbischoff/false.alarm/tree/d34d2ab00bf3b0588362a0ca9660501e79b786b7" target="_blank">d34d2ab</a>.
+The results in this page were generated with repository version <a href="https://github.com/franzbischoff/false.alarm/tree/c772fa09b420cee4c915f894088679aa4c0ea41b" target="_blank">c772fa0</a>.
 See the <em>Past versions</em> tab to see a history of the changes made to the
 R Markdown and HTML files.
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 <h3><span class="header-section-number">1.3.3</span> Importance analysis</h3>
 <p>Using the three techniques above simultaneously allows a broad comparison of the model behavior<span class="citation"><sup><a href="#ref-Greenwell2020" role="doc-biblioref">4</a></sup></span>. All three methods are model-agnostic (separates interpretation from the model), but as we have seen above, each method has its advantages and disadvantages<span class="citation"><sup><a href="#ref-Molnar2022" role="doc-biblioref">5</a></sup></span>.</p>
 <p>Fig. <a href="#fig:importance">1.5</a> then shows the variable importance using three methods: Feature Importance Ranking Measure (FIRM) using Individual Conditional Expectation (ICE), Permutation-based, and Shapley Additive explanations (SHAP). The first line of this figure shows that the interaction between <code>mp_threshold</code> and <code>window_size</code> obscures the results, where except for <code>time_constraint</code>, the other variables have similar importance. In the second line, the most important feature that all three methods agree on is the <code>regime_threshold</code>.</p>
-<pre><code>Warning: Removed 31 rows containing non-finite values (`stat_boxplot()`).</code></pre>
 <div class="figure" style="text-align: center"><span style="display:block;" id="fig:importance"></span>
-<img src="figure/regime_optimize.Rmd/importance-1.svg" alt="Variables importances using three different methods. A) Feature Importance Ranking Measure using ICE curves. B) Permutation method. C) SHAP (400 iterations). Line 1 refers to the original fit, and line 2 to the re-fit, taking into account the interactions between variables (Fig. \@ref(fig:interaction))." width="100%" />
+<img src="figure/regime_optimize.Rmd/importance-1.svg" alt="Variables importances using three different methods. A) Feature Importance Ranking Measure using ICE curves. B) Permutation method. C) SHAP (100 iterations). Line 1 refers to the original fit, and line 2 to the re-fit, taking into account the interactions between variables (Fig. \@ref(fig:interaction))." width="100%" />
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-Figure 1.5: Variables importances using three different methods. A) Feature Importance Ranking Measure using ICE curves. B) Permutation method. C) SHAP (400 iterations). Line 1 refers to the original fit, and line 2 to the re-fit, taking into account the interactions between variables (Fig. <a href="#fig:interaction">1.4</a>).
+Figure 1.5: Variables importances using three different methods. A) Feature Importance Ranking Measure using ICE curves. B) Permutation method. C) SHAP (100 iterations). Line 1 refers to the original fit, and line 2 to the re-fit, taking into account the interactions between variables (Fig. <a href="#fig:interaction">1.4</a>).
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+ date     2023-11-30
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 ─ Packages ───────────────────────────────────────────────────────────────────
@@ -1951,15 +2047,15 @@ <h1>References</h1>
  knitr          1.43        2023-05-25 [1] CRAN (R 4.3.0)
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  lhs            1.1.6       2022-12-17 [1] CRAN (R 4.3.0)
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  MASS           7.3-60      2023-05-04 [2] CRAN (R 4.3.1)
- Matrix         1.6-0       2023-07-08 [2] CRAN (R 4.3.1)
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  memoise        2.0.1       2021-11-26 [1] CRAN (R 4.3.0)
  mgcv           1.9-0       2023-07-11 [2] CRAN (R 4.3.1)
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- rpart          4.1.19      2022-10-21 [2] CRAN (R 4.3.0)
+ rmarkdown      2.25.1      2023-10-10 [1] Github (rstudio/rmarkdown@65a352e)
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@@ -2004,7 +2100,7 @@ <h1>References</h1>
  signal         0.7-7       2021-05-25 [1] CRAN (R 4.3.0)
  stringi        1.7.12      2023-01-11 [1] CRAN (R 4.3.1)
  stringr        1.5.0       2022-12-02 [1] CRAN (R 4.3.1)
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