Building Predictive Models in R Using the caret Package
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The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R to simplify model training and tuning across a wide variety of modeling techniques.Abstract:
The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R. The package focuses on simplifying model training and tuning across a wide variety of modeling techniques. It also includes methods for pre-processing training data, calculating variable importance, and model visualizations. An example from computational chemistry is used to illustrate the functionality on a real data set and to benchmark the benefits of parallel processing with several types of models.read more
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Disentangling the Effects of Monounsaturated Fatty Acids from Other Components of a Mediterranean Diet on Serum Metabolite Profiles: A Randomized Fully Controlled Dietary Intervention in Healthy Subjects at Risk of the Metabolic Syndrome.
TL;DR: It is demonstrated that the MUFA component is responsible for reducing LDL subclasses and fractions, and therefore causes an antiatherogenic lipid profile, and Interestingly, consumption of the other components in the MED diet show additional health effects.
Journal ArticleDOI
Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods.
Matheus Henrique Dal Molin Ribeiro,Matheus Henrique Dal Molin Ribeiro,Viviana Cocco Mariani,Viviana Cocco Mariani,Leandro dos Santos Coelho,Leandro dos Santos Coelho +5 more
TL;DR: A hybrid learning framework is developed to forecast multi-step-ahead meningitis cases in four states of Brazil and showed that combining EEMD, heterogeneous ensemble and WI with weights obtained by optimization can develop precise and stable forecasts.
Journal ArticleDOI
Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques
TL;DR: The SVM-based model outperformed the analytical method for the TAN prediction, and showed higher prediction accuracy in comparison with Artificial Neural Networks, revealing the future promise of SVM for prediction in non-linear and dynamic AD processes.
Journal ArticleDOI
Conditional inference trees for knowledge extraction from motor health condition data
TL;DR: A specific kind of decision tree algorithm, called conditional inference tree, is used to extract relevant knowledge from data that pertains to electrical motors, chosen due to its flexibility, strong statistical foundation, as well as great capabilities to generalize and cope with problems in the data.
Journal ArticleDOI
How to Address the Data Quality Issues in Regression Models: A Guided Process for Data Cleaning
TL;DR: A process for data cleaning in regression models (DC-RM), where the dataset that is cleaned was used to train the same regression models proposed by the authors of UCI datasets and the results achieved are better than or equal to that presented by the datasets’ authors.
References
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Modern Applied Statistics with S
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