<p>It highlights the two nested loops. The outer cross-validation loop provides 10 performance estimates from predicting the corresponding test set by the optimized model built in the inner 20 fold cross-validation loop. The data set used to build and tune the model in the inner cross-validation loop is completely independent of the test set used in the outer iteration.</p
<p>Illustrative scheme of the 10-fold cross validation procedure. During the 10 iterations each samp...
Contains all code and data to reproduce the results shown in the manuscript "Explaining the optimist...
<p>Prediction performance of 10-fold cross-validation based on different encoding methods.</p
<p>The upper panel illustrates the combination of the inner cross-validation loop, which is used to ...
<p>Our analysis employed a double look cross-validation. The inner loop determines the optimal numbe...
The inner loop performs cross-validation to identify the best features and model hyper-parameters us...
<p>In each iteration, data are divided into training and test sets. Before training, another (inner)...
<p>The data set is partitioned into 10 parts (folds) in the outer loop. One fold of the data set is ...
The “inner” cross-validation: The “inner” cross-validation is for model selection based on their acc...
Performance of different models on PF2095 dataset using 10-fold cross-validation method.</p
Performance of different models on PF4204 dataset using 10-fold cross-validation method.</p
<p>Cross-validated performance estimates for single-source and multi-source models.</p
<p>First, different models are trained and validated with cross-validation and the best set of param...
Performance of different modules on training sets using 5-fold cross-validation.</p
<p>The prediction performance of the final model using 18 features, by 10-fold cross validation.</p
<p>Illustrative scheme of the 10-fold cross validation procedure. During the 10 iterations each samp...
Contains all code and data to reproduce the results shown in the manuscript "Explaining the optimist...
<p>Prediction performance of 10-fold cross-validation based on different encoding methods.</p
<p>The upper panel illustrates the combination of the inner cross-validation loop, which is used to ...
<p>Our analysis employed a double look cross-validation. The inner loop determines the optimal numbe...
The inner loop performs cross-validation to identify the best features and model hyper-parameters us...
<p>In each iteration, data are divided into training and test sets. Before training, another (inner)...
<p>The data set is partitioned into 10 parts (folds) in the outer loop. One fold of the data set is ...
The “inner” cross-validation: The “inner” cross-validation is for model selection based on their acc...
Performance of different models on PF2095 dataset using 10-fold cross-validation method.</p
Performance of different models on PF4204 dataset using 10-fold cross-validation method.</p
<p>Cross-validated performance estimates for single-source and multi-source models.</p
<p>First, different models are trained and validated with cross-validation and the best set of param...
Performance of different modules on training sets using 5-fold cross-validation.</p
<p>The prediction performance of the final model using 18 features, by 10-fold cross validation.</p
<p>Illustrative scheme of the 10-fold cross validation procedure. During the 10 iterations each samp...
Contains all code and data to reproduce the results shown in the manuscript "Explaining the optimist...
<p>Prediction performance of 10-fold cross-validation based on different encoding methods.</p