<p>Results of Maxent model showing jackknife tests of variable importance for training samples.</p
<p>The graph shows the result of the Jackknife test of variable importance using regularized trainin...
<p>Bars indicate the explanatory power (in terms of gain) when only a single predictor is included i...
Performance of machine learning models on test set using the ROSE-adjusted balanced training set.</p
<p>This figure shows the results of the jackknife procedure on the full Maxent model.</p
<p>Each curve represents the regularized training gain of each variable used in isolation for each s...
Jackknife test showing the importance of each variable independently, when building the Maxent model...
<p>Jackknife tests (%) and the mean feature numbers of the SVM-based predictors for 30 samples.</p
<p>Results of jackknife test of relative importance of predictor variables for Chinese caterpillar f...
Blue bar = Shows importance of each variables in explaining the variation in the data when used sepa...
<p>Coefficients represent the relative importance of the explanatory variables (the sum of coefficie...
<p>Summary of results based on the jackknife and 5-fold cross validation (5-cv) tests on the trainin...
<p>For each species, the table shows the area under the curve (AUC) and regularized training gain (G...
Performance of machine learning models on test set using the original imbalanced training set.</p
<p>The figures show each variable’s contribution to (A) regularized training gain, and (B) test AUC ...
<p>The performance of different features on PDB1075 dataset (Jackknife test evaluation).</p
<p>The graph shows the result of the Jackknife test of variable importance using regularized trainin...
<p>Bars indicate the explanatory power (in terms of gain) when only a single predictor is included i...
Performance of machine learning models on test set using the ROSE-adjusted balanced training set.</p
<p>This figure shows the results of the jackknife procedure on the full Maxent model.</p
<p>Each curve represents the regularized training gain of each variable used in isolation for each s...
Jackknife test showing the importance of each variable independently, when building the Maxent model...
<p>Jackknife tests (%) and the mean feature numbers of the SVM-based predictors for 30 samples.</p
<p>Results of jackknife test of relative importance of predictor variables for Chinese caterpillar f...
Blue bar = Shows importance of each variables in explaining the variation in the data when used sepa...
<p>Coefficients represent the relative importance of the explanatory variables (the sum of coefficie...
<p>Summary of results based on the jackknife and 5-fold cross validation (5-cv) tests on the trainin...
<p>For each species, the table shows the area under the curve (AUC) and regularized training gain (G...
Performance of machine learning models on test set using the original imbalanced training set.</p
<p>The figures show each variable’s contribution to (A) regularized training gain, and (B) test AUC ...
<p>The performance of different features on PDB1075 dataset (Jackknife test evaluation).</p
<p>The graph shows the result of the Jackknife test of variable importance using regularized trainin...
<p>Bars indicate the explanatory power (in terms of gain) when only a single predictor is included i...
Performance of machine learning models on test set using the ROSE-adjusted balanced training set.</p