In order to reduce dependencies, model coefficients are now tidied with the parameters package instead of broom and broom.mixed. Thanks to @IndrajeetPatil for the contributions. In cross_validate() and cross_validate_fn(), fold columns can now have a varying number of folds in repeated cross-validation. Struggling to choose a number of folds? Average over multiple settings. In the Class Level Results in multinomial evaluations, the nested Confusion Matrix and Results tibbles are now named with their class to ease extraction and further work with these tibbles. The Results tibble further gets a Class column. This information might be redundant, but could make life easier. Adds vignette: Multiple-k: Picking the number of folds for cross-va...
*<p>ReliefF and MRMR are ranking procedures. The optimal sets of features for these two methods were...
plot_confusion_matrix(): Breaking: Adds slight 3D tile effect to help separate tiles with the same ...
The inner loop performs cross-validation to identify the best features and model hyper-parameters us...
cross_validate_fn() is added. Cross-validate custom model functions. Breaking change: In evaluate()...
evaluate() is added. Evaluate your model's predictions with the same metrics as used in cross_valida...
In plot_confusion_matrix(), adds option to only have row and column percentages in the diagonal tile...
Fixes documentation in cross_validate_fn(). The examples section contained an unreasonable number of...
<p>The data set is partitioned into 10 parts (folds) in the outer loop. One fold of the data set is ...
The K-fold Cross Validation (KCV) technique is one of the most used approaches by practitioners for ...
<p>Column 2, the MCC obtained in the 5-fold cross validation (CV) by each of the 10 models. Column 3...
Breaking Breaking change: In plot_confusion_matrix(), the targets_col and predictions_col arguments...
*<p>ReliefF and MRMR are ranking procedures. The optimal sets of features for these two methods were...
Cross-validation (CV) type of methods have been widely used to facilitate model estimation and varia...
In the machine learning field the performance of a classifier is usually measured in terms of predic...
Cross-validation (CV) is a common approach for determining the optimal number of components in a pri...
*<p>ReliefF and MRMR are ranking procedures. The optimal sets of features for these two methods were...
plot_confusion_matrix(): Breaking: Adds slight 3D tile effect to help separate tiles with the same ...
The inner loop performs cross-validation to identify the best features and model hyper-parameters us...
cross_validate_fn() is added. Cross-validate custom model functions. Breaking change: In evaluate()...
evaluate() is added. Evaluate your model's predictions with the same metrics as used in cross_valida...
In plot_confusion_matrix(), adds option to only have row and column percentages in the diagonal tile...
Fixes documentation in cross_validate_fn(). The examples section contained an unreasonable number of...
<p>The data set is partitioned into 10 parts (folds) in the outer loop. One fold of the data set is ...
The K-fold Cross Validation (KCV) technique is one of the most used approaches by practitioners for ...
<p>Column 2, the MCC obtained in the 5-fold cross validation (CV) by each of the 10 models. Column 3...
Breaking Breaking change: In plot_confusion_matrix(), the targets_col and predictions_col arguments...
*<p>ReliefF and MRMR are ranking procedures. The optimal sets of features for these two methods were...
Cross-validation (CV) type of methods have been widely used to facilitate model estimation and varia...
In the machine learning field the performance of a classifier is usually measured in terms of predic...
Cross-validation (CV) is a common approach for determining the optimal number of components in a pri...
*<p>ReliefF and MRMR are ranking procedures. The optimal sets of features for these two methods were...
plot_confusion_matrix(): Breaking: Adds slight 3D tile effect to help separate tiles with the same ...
The inner loop performs cross-validation to identify the best features and model hyper-parameters us...