modelMatch {SAiVE}

Find machine learning models for use in caret

Description

As of 2023-06-15, there are 238 different machine learning models which can be used with the CARET package. As evaluating model performance is time consuming, selecting a subset of models to test prior to deciding on which model to use is essential. This function aims to facilitate this process by matching models according to their Jaccard similarity, in a process inspired by this section in the CARET e-book. Model data is fetched from here. The result of this function can then be passed to spatPredict() to further refine model selection.

Usage

modelMatch(model, type = "match", similarity = 0.7)

Arguments

model

The abbreviation or short name of the model you'd like to match, taken from here.

type

The type of model. You can match the input model type with "match", or select from dual-purpose models ("dual"), regression models only ("regression"), or classification models only ("classification").

similarity

The similarity threshold to use as a numeric value from 0 to 1. Models with a similarity score greater than this will be returned.

Details

This function requires internet access to get an up-to-date list of models.

Value

A data.frame of models meeting the requested similarity threshold along with the model abbreviations that can be passed to caret::train() or to function spatPredict().

Author(s)

Ghislain de Laplante ([e-mail] or [e-mail])

Examples

# Find models similar to 'ranger'
modelMatch("ranger")

# Find only models with a similarity > 0.8 to 'ranger'
modelMatch("ranger", similarity = 0.8)