Showing 3 of total 3 results (show query)
boost-r
mboost:Model-Based Boosting
Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. Models and algorithms are described in <doi:10.1214/07-STS242>, a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>. The package allows user-specified loss functions and base-learners.
Maintained by Torsten Hothorn. Last updated 5 months ago.
boosting-algorithmsgamglmmachine-learningmboostmodellingr-languagetutorialsvariable-selectionopenblas
72 stars 12.70 score 540 scripts 27 dependents
philipppro
measures:Performance Measures for Statistical Learning
Provides the biggest amount of statistical measures in the whole R world. Includes measures of regression, (multiclass) classification and multilabel classification. The measures come mainly from the 'mlr' package and were programed by several 'mlr' developers.
Maintained by Philipp Probst. Last updated 4 years ago.
1 stars 4.43 score 88 scripts 2 dependents
drordas
D2MCS:Data Driving Multiple Classifier System
Provides a novel framework to able to automatically develop and deploy an accurate Multiple Classifier System based on the feature-clustering distribution achieved from an input dataset. 'D2MCS' was developed focused on four main aspects: (i) the ability to determine an effective method to evaluate the independence of features, (ii) the identification of the optimal number of feature clusters, (iii) the training and tuning of ML models and (iv) the execution of voting schemes to combine the outputs of each classifier comprising the Multiple Classifier System.
Maintained by Miguel Ferreiro-Díaz. Last updated 3 years ago.
3.70 score