DOT - Render and Export DOT Graphs in R
Renders DOT diagram markup language in R and also provides the possibility to export the graphs in PostScript and SVG (Scalable Vector Graphics) formats. In addition, it supports literate programming packages such as 'knitr' and 'rmarkdown'.
Last updated 2 years ago
5.26 score 2 stars 1 packages 60 scripts 290 downloads
shapley - Weighted Mean SHAP for Feature Selection in ML Grid and Ensemble
This R package introduces Weighted Mean SHapley Additive exPlanations (WMSHAP), an innovative method for calculating SHAP values for a grid of fine-tuned base-learner machine learning models as well as stacked ensembles, a method not previously available due to the common reliance on single best-performing models. By integrating the weighted mean SHAP values from individual base-learners comprising the ensemble or individual base-learners in a tuning grid search, the package weights SHAP contributions according to each model's performance, assessed by multiple either R squared (for both regression and classification models). alternatively, this software also offers weighting SHAP values based on the area under the precision-recall curve (AUCPR), the area under the curve (AUC), and F2 measures for binary classifiers. It further extends this framework to implement weighted confidence intervals for weighted mean SHAP values, offering a more comprehensive and robust feature importance evaluation over a grid of machine learning models, instead of solely computing SHAP values for the best model. This methodology is particularly beneficial for addressing the severe class imbalance (class rarity) problem by providing a transparent, generalized measure of feature importance that mitigates the risk of reporting SHAP values for an overfitted or biased model and maintains robustness under severe class imbalance, where there is no universal criteria of identifying the absolute best model. Furthermore, the package implements hypothesis testing to ascertain the statistical significance of SHAP values for individual features, as well as comparative significance testing of SHAP contributions between features. Additionally, it tackles a critical gap in feature selection literature by presenting criteria for the automatic feature selection of the most important features across a grid of models or stacked ensembles, eliminating the need for arbitrary determination of the number of top features to be extracted. This utility is invaluable for researchers analyzing feature significance, particularly within severely imbalanced outcomes where conventional methods fall short. Moreover, it is also expected to report democratic feature importance across a grid of models, resulting in a more comprehensive and generalizable feature selection. The package further implements a novel method for visualizing SHAP values both at subject level and feature level as well as a plot for feature selection based on the weighted mean SHAP ratios.
Last updated 1 months ago
class-imbalanceclass-imbalance-problemfeature-extractionfeature-importancefeature-selectionmachine-learningmachine-learning-algorithmsshapshap-analysisshap-valuesshapelyshapley-additive-explanationsshapley-decompositionshapley-valueshapley-valuesshapleyvalueweighted-shapweighted-shap-confidence-intervalweighted-shapleyweighted-shapley-ci
5.12 score 12 stars 17 scripts 700 downloadsmlim - Single and Multiple Imputation with Automated Machine Learning
Machine learning algorithms have been used for performing single missing data imputation and most recently, multiple imputations. However, this is the first attempt for using automated machine learning algorithms for performing both single and multiple imputation. Automated machine learning is a procedure for fine-tuning the model automatic, performing a random search for a model that results in less error, without overfitting the data. The main idea is to allow the model to set its own parameters for imputing each variable separately instead of setting fixed predefined parameters to impute all variables of the dataset. Using automated machine learning, the package fine-tunes an Elastic Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting, or Stacked Ensemble machine learning model (from one or a combination of other supported algorithms) for imputing the missing observations. This procedure has been implemented for the first time by this package and is expected to outperform other packages for imputing missing data that do not fine-tune their models. The multiple imputation is implemented via bootstrapping without letting the duplicated observations to harm the cross-validation procedure, which is the way imputed variables are evaluated. Most notably, the package implements automated procedure for handling imputing imbalanced data (class rarity problem), which happens when a factor variable has a level that is far more prevalent than the other(s). This is known to result in biased predictions, hence, biased imputation of missing data. However, the autobalancing procedure ensures that instead of focusing on maximizing accuracy (classification error) in imputing factor variables, a fairer procedure and imputation method is practiced.
Last updated 4 months ago
automatic-machine-learningautomlclassimbalancedata-scienceelastic-netextreme-gradient-boostinggbmglmgradient-boostinggradient-boosting-machineimputationimputation-algorithmimputation-methodsmachine-learningmissing-datamultipleimputationstack-ensemble
4.65 score 30 stars 7 scripts 226 downloadsautoEnsemble - Automated Stacked Ensemble Classifier for Severe Class Imbalance
An AutoML algorithm is developed to construct homogeneous or heterogeneous stacked ensemble models using specified base-learners. Various criteria are employed to identify optimal models, enhancing diversity among them and resulting in more robust stacked ensembles. The algorithm optimizes the model by incorporating an increasing number of top-performing models to create a diverse combination. Presently, only models from 'h2o.ai' are supported.
Last updated 4 months ago
aialgorithmautomated-machine-learningautomlautoml-algorithmsensembleensemble-learningh2oh2oaimachine-learningmachinelearningmetalearningstack-ensemblestacked-ensemblesstacking
4.32 score 5 stars 21 scripts 204 downloadsmd.log - Produces Markdown Log File with a Built-in Function Call
Produces clean and neat Markdown log file and also provide an argument to include the function call inside the Markdown log.
Last updated 2 years ago
4.13 score 1 stars 1 packages 1 scripts 184 downloadsh2otools - Machine Learning Model Evaluation for 'h2o' Package
In the process of model selection, the common practice is to select a model with the higher performance. However, the fine-tuning process might tune multiple models with negligible performance differences. This software provides a statistical procedure for comparing the performance of machine learning models using a bootstrapping technique to assess significant differences between models' performances. Additionally, it offers extra performance metrics such as the F-Measure and additional functionalities for working with the H2O AI software package. For more information about the 'h2o' package, visit https://h2o.ai/.
Last updated 5 months ago
3.80 score 1 stars 1 packages 14 scripts 271 downloads
