<p>Jacknife analysis for <i>Montipora</i>, <i>Leptoseris</i> and <i>Porites</i> showing the mean AUC when a single variable is used to develop a model (red, brown and green bars) or excluded from the modeling process (gray bars). This process of inclusion and exclusion isolates the contribution of each predictor variable from the other variables, and describes whether a particular variable improves or degrades the performance of a model. The AUC value for a single variable model is depicted inside the bars. The error bars denote one standard error (based on 10 model iterations).</p
<p>Performance of the model when constraining the maximum number of variables to be included in the ...
<p><b>1, 3, and 5.</b> In (a) the mean values of across 46 ADDs from <a href="http://www.plosone.or...
This paper is concerned with the application of jackknife methods as a means of bias reduction in th...
<p>For each species, the table shows the area under the curve (AUC) and regularized training gain (G...
<p>Higher values for the regularized training gain of the jack-knife test indicates greater contribu...
<p>Higher values for the regularized training gain of the jack-knife test indicates greater contribu...
Blue bar = Shows importance of each variables in explaining the variation in the data when used sepa...
<p>Bars indicate the explanatory power (in terms of gain) when only a single predictor is included i...
<p>The graph shows the result of the Jackknife test of variable importance using regularized trainin...
<p>The figures show each variable’s contribution to (A) regularized training gain, and (B) test AUC ...
Jackknife test showing the importance of each variable independently, when building the Maxent model...
When using linear models, a common practice is to find the single best model fit used in predictions...
<p>The AUC for voting before (red lines) and after (blue lines) an answer is accepted, as well as ac...
<p>Proportion of species that did not achieve the neither the model-specific target nor the 10% of t...
<p><sup>a</sup> Predictors in the Model: (Constant), MOCA.</p><p><sup>b</sup> Predictors in the Mode...
<p>Performance of the model when constraining the maximum number of variables to be included in the ...
<p><b>1, 3, and 5.</b> In (a) the mean values of across 46 ADDs from <a href="http://www.plosone.or...
This paper is concerned with the application of jackknife methods as a means of bias reduction in th...
<p>For each species, the table shows the area under the curve (AUC) and regularized training gain (G...
<p>Higher values for the regularized training gain of the jack-knife test indicates greater contribu...
<p>Higher values for the regularized training gain of the jack-knife test indicates greater contribu...
Blue bar = Shows importance of each variables in explaining the variation in the data when used sepa...
<p>Bars indicate the explanatory power (in terms of gain) when only a single predictor is included i...
<p>The graph shows the result of the Jackknife test of variable importance using regularized trainin...
<p>The figures show each variable’s contribution to (A) regularized training gain, and (B) test AUC ...
Jackknife test showing the importance of each variable independently, when building the Maxent model...
When using linear models, a common practice is to find the single best model fit used in predictions...
<p>The AUC for voting before (red lines) and after (blue lines) an answer is accepted, as well as ac...
<p>Proportion of species that did not achieve the neither the model-specific target nor the 10% of t...
<p><sup>a</sup> Predictors in the Model: (Constant), MOCA.</p><p><sup>b</sup> Predictors in the Mode...
<p>Performance of the model when constraining the maximum number of variables to be included in the ...
<p><b>1, 3, and 5.</b> In (a) the mean values of across 46 ADDs from <a href="http://www.plosone.or...
This paper is concerned with the application of jackknife methods as a means of bias reduction in th...