<p>The variable importance is calculated by comparing the mean squared error from models with the original dataset with the mean squared error from models with an altered dataset where the predictor variable is randomly permuted. Acronyms: AET = actual evapotranspiration, PC00-10 = Change in population density between 2000 and 2010, GDP/Area = gross domestic product per km<sup>2</sup>, HII = Human Influence Index.</p
Importance is given as mean decrease in accuracy in percent showing the loss of accuracy when removi...
Variable importance graphs are great tool to see, in a model, which variables are interesting. Since...
(A) Importance ranking of the included variables. (B) Ten-fold cross-validation of the random forest...
<p>Variable importance for the previous abundance variables from the random forest analysis examinin...
<p>The model including variables here was used to produce the density weighting layer for the dasyme...
<p>Included variables are listed on the vertical axis, with corresponding variable importance for ea...
Variable importance measured according the pseudo-r2 values obtained from the Random Forest model.</...
A major focus in statistics is building and improving computational algorithms that can use data to ...
A major focus in statistics is building and improving computational algorithms that can use data to ...
A major focus in statistics is building and improving computational algorithms that can use data to ...
<p>Importance was calculated based on mean decrease in model accuracy (black bars) and mean decrease...
<p><i>Footnote.</i> Numerical values represent % increase in mean squared error if variable is omitt...
<p>For both grasses and woody plants shallow (0–20 cm) soil moisture and soil type (<i>i</i>.<i>e</i...
<p>Higher values of the “mean decrease in accuracy” and the “mean decrease in Gini index” indicate h...
<p>Importance of the explanatory variables in the Random Forest models for β-site and β-conn measure...
Importance is given as mean decrease in accuracy in percent showing the loss of accuracy when removi...
Variable importance graphs are great tool to see, in a model, which variables are interesting. Since...
(A) Importance ranking of the included variables. (B) Ten-fold cross-validation of the random forest...
<p>Variable importance for the previous abundance variables from the random forest analysis examinin...
<p>The model including variables here was used to produce the density weighting layer for the dasyme...
<p>Included variables are listed on the vertical axis, with corresponding variable importance for ea...
Variable importance measured according the pseudo-r2 values obtained from the Random Forest model.</...
A major focus in statistics is building and improving computational algorithms that can use data to ...
A major focus in statistics is building and improving computational algorithms that can use data to ...
A major focus in statistics is building and improving computational algorithms that can use data to ...
<p>Importance was calculated based on mean decrease in model accuracy (black bars) and mean decrease...
<p><i>Footnote.</i> Numerical values represent % increase in mean squared error if variable is omitt...
<p>For both grasses and woody plants shallow (0–20 cm) soil moisture and soil type (<i>i</i>.<i>e</i...
<p>Higher values of the “mean decrease in accuracy” and the “mean decrease in Gini index” indicate h...
<p>Importance of the explanatory variables in the Random Forest models for β-site and β-conn measure...
Importance is given as mean decrease in accuracy in percent showing the loss of accuracy when removi...
Variable importance graphs are great tool to see, in a model, which variables are interesting. Since...
(A) Importance ranking of the included variables. (B) Ten-fold cross-validation of the random forest...