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Topic :: Regression Models [Remove This Filter]
Topic :: Machine Learning :: Model Selection and Validation [Remove This Filter]
5 projects in result set.
0. Classification and Regression Training - The caret packages contain functions for tuning predictive models, pre-processing, variable importance and other tools related to machine learning and pattern recognition. Parallel processing versions of the main package are also included.
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- Development Status : 5 - Production/Stable [Filter]
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- Topic : Machine Learning : Model Selection and Validation (Now Filtering)
- Topic : Regression Models (Now Filtering)
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Registered: 2008-06-05 19:31 |
1. Tools for Uplift Modeling - The R Package tools4uplift integrates some tools for exploring and modeling uplift. The content can be separated into the following steps of statistical modeling: quantization, visualization, model selection and model validation.
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- Development Status : 4 - Beta [Filter]
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- Topic : Graphics : Plotting [Filter]
- Topic : Machine Learning : Model Selection and Validation (Now Filtering)
- Topic : Regression Models (Now Filtering)
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Registered: 2018-08-31 21:23 |
2. addendum - Utility functions that should have been in R: easy glmnet, pretty graphing and swift debugging and profiling.
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- Topic : Machine Learning : Model Selection and Validation (Now Filtering)
- Topic : Multivariate Statistics : Missing Data [Filter]
- Topic : Regression Models (Now Filtering)
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Registered: 2011-06-17 13:35 |
3. Finite Mixure of AFT and FMR models - FMRS package provides estimation and variable selection in Finite Mixture of Accelerated Failure Time Regression (FMAFTR) and Finite Mixture of Regression (FMR) models with a large number of covariates and/or right censoring and heterogeneous structure.
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- Development Status : 5 - Production/Stable [Filter]
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- License : OSI Approved : GNU General Public License (GPL) [Filter]
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- Operating System : Microsoft : Windows [Filter]
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- Programming Language : R [Filter]
- Topic : Machine Learning : Model Selection and Validation (Now Filtering)
- Topic : Regression Models (Now Filtering)
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Registered: 2016-03-07 02:31 |
4. The Degrees of Freedom of PLS - The package provides Degrees of Freedom of Partial Least Squares. Model selection based on information criteria can be performed.
Krämer & Braun (2007) Kernelizing PLS, Degrees of Freedom, and Efficient Model Selection, ICML-07, 441 - 448
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- Development Status : 1 - Planning [Filter]
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- Topic : Machine Learning : Model Selection and Validation (Now Filtering)
- Topic : Multivariate Statistics : Projection Methods [Filter]
- Topic : Regression Models (Now Filtering)
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Registered: 2009-10-08 14:16 |
