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gencve (version 0.3)

General Cross Validation Engine

Description

Engines for cross-validation of many types of regression and class prediction models are provided. These engines include built-in support for 'glmnet', 'lars', 'plus', 'MASS', 'rpart', 'C50' and 'randomforest'. It is easy for the user to add other regression or classification algorithms. The 'parallel' package is used to improve speed. Several data generation algorithms for problems in regression and classification are provided.

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Version

Install

install.packages('gencve')

Monthly Downloads

19

Version

0.3

License

GPL (>= 2)

Maintainer

Last Published

April 11th, 2016

Functions in gencve (0.3)

General Cross Validation Engine \Sexpr[results=rd,stage=build]{tools:::Rd_package_title("#1")}gencveGeneral Cross Validation Engine

Detroit Homicide Data for 1961-73

Mean Absolute Percentage Error

Random XOR Samples

Variance Inflation Factor

CART Prediction

Data on Children who have had Corrective Spinal Surgery

Singh Prostate Microarray Test Data

kNN or NN prediction

Customer Churn Data

Logistic Regression and Regularized Logistic Regression Prediction

Feature Select For Wide Data

Elastic Net Regression Prediction

Mean Square Error Loss

Mean Absolute Error

BFOS Digit Recognition Problem

Synthetic Regression Data

Estimate EPE Using Delete-d Cross-Validation

Backward Stagewise Regression with AIC or BIC

Select k with Leave-one-out CV

CART regression prediction

misclassificationrate

Misclassification Rate for Class Prediction

Estimate Misclassification Rate Using d-fold Cross-Validation for Class Prediction

Shao Holdout Sample Size for Linear Regression Variable Selection

Random Mixture Classification Example

Pollution Data from McDonald and Schwing

Fit LASSO Regression using Mallows Cp and Predict

Support Vector Machine Prediction

Prostate Cancer Data

Regression EPE for All Implemented Methods

Random Forest Prediction

Fit Random Forest Regression Predictor

Naive Bayes Prediction

Forest Fires in Montesinho Natural Park

Singh Prostate Microarray Training Data

SCAD or MCP Regression Prediction

Linear Predictor using Least-Squares Regression

Support Vector Machine Regression Prediction

Mean Absolute Percentage Error

Meat Spectrometry to Determine Fat Content

LDA predictions

Nearest Neighbour Prediction

log-loss function for multiclass prediction