R-universe search: topic:automatic-machine-learning

Showing 3 of total 3 results (show query)

spsanderson

tidyAML:Automatic Machine Learning with 'tidymodels'

The goal of this package will be to provide a simple interface for automatic machine learning that fits the 'tidymodels' framework. The intention is to work for regression and classification problems with a simple verb framework.

Maintained by Steven Sanderson. Last updated 11 months ago.

automatic-machine-learning automl classification machine-learning parsnip r-language r-programming regression tidy tidymodels tidyverse

68 stars 6.56 score 36 scripts 1 dependents

r-tensorflow

autokeras:R Interface to 'AutoKeras'

R Interface to 'AutoKeras' <https://autokeras.com/>. 'AutoKeras' is an open source software library for Automated Machine Learning (AutoML). The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. 'AutoKeras' provides functions to automatically search for architecture and hyperparameters of deep learning models.

Maintained by Juan Cruz Rodriguez. Last updated 4 years ago.

autodl automatic-machine-learning automl deep-learning keras machine-learning tensorflow

73 stars 5.34 score

haghish

mlim: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.

Maintained by E. F. Haghish. Last updated 8 months ago.

automatic-machine-learning automl classimbalance data-science elastic-net extreme-gradient-boosting gbm glm gradient-boosting gradient-boosting-machine imputation imputation-algorithm imputation-methods machine-learning missing-data multipleimputation stack-ensemble

31 stars 4.49 score 7 scripts