# rknn v1.2-1

0

0th

Percentile

## Random KNN Classification and Regression

Random knn classification and regression are implemented. Random knn based feature selection methods are also included. The approaches are mainly developed for high-dimensional data with small sample size.

## Functions in rknn

 Name Description plot rknn support Plot Function for Support Criterion lambda Compute Number of Silent Features normalize Data Normalization cv.coef Coefficient of Variation print.rknn Print method for Random KNN rknnSupport Support Criterion eta Coverage Probability rknn-package Random KNN Classification and Regression bestset Extract the Best Subset of Feature from Selection Process rknn Random KNN Classification and Regression internal functions Random KNN Internal Functions PRESS Predicted Residual Sum of Squares rsqp Predicted R-square print.rknnBE Print Method for Recursive Backward Elimination Feature Selection plot backward elimination Plot Function for Recursive Backward Elimination Feature Selection varUsed Features Used or Not Used in Random KNN r Choose number of KNNs fitted Extract Model Fitted Values print.rknnSupport Print Method for Random KNN Support Criterion rknnBeg Backward Elimination Feature Selection with Random KNN predicted Prediced Value From a Linear Model confusion Classification Confusion Matrix and Accuracy No Results!