func_categorical_preds

<p>OPTION to convert categorical features TO either numeric [ if levels more than 32] OR to dummy variables [ if levels less than 32 ]</p>

internal

A Non-Metric Space Library ('NMSLIB' <https://github.com/searchivarius/nmslib>) wrapper, which according to the authors "is an efficient cross-platform similarity search library and a toolkit for evaluation of similarity search methods. The goal of the 'NMSLIB' <https://github.com/searchivarius/nmslib> Library is to create an effective and comprehensive toolkit for searching in generic non-metric spaces. Being comprehensive is important, because no single method is likely to be sufficient in all cases. Also note that exact solutions are hardly efficient in high dimensions and/or non-metric spaces. Hence, the main focus is on approximate methods". The wrapper also includes Approximate Kernel k-Nearest-Neighbor functions based on the 'NMSLIB' <https://github.com/searchivarius/nmslib> 'Python' Library.

Lampros Mouselimis

nmslibR

Non Metric Space (Approximate) Library

B. Naidan

L. Boytsov

Yu. Malkov

B. Frederickson

D. Novak

func_categorical_preds function

OPTION to convert categorical features TO either numeric [ if levels more than 32] OR to dummy variables [ if levels less than 32 ] — func_categorical_preds

OPTION to convert categorical features TO either numeric [ if levels more than 32] OR to dummy variables [ if levels less than 32 ]

func_categorical_preds: OPTION to convert categorical features TO either numeric [ if levels more than 32] OR to dummy variables [ if levels less than 32 ]

Description

Usage

Arguments