Classification

library(unifiedml)
library(randomForest)

## randomForest 4.7-1.2

## Type rfNews() to see new features/changes/bug fixes.

library(e1071)
library(caret)

## Loading required package: ggplot2

## 
## Attaching package: 'ggplot2'

## The following object is masked from 'package:randomForest':
## 
##     margin

## Loading required package: lattice

set.seed(123)

X <- iris[, 1:4]
y <- iris$Species

models <- list(
  glm  = Model$new(caret::train),
  rf   = Model$new(randomForest::randomForest),
  svm  = Model$new(e1071::svm)   
)

params <- list(
  glm = list(method = "glmnet",
             tuneGrid = data.frame(alpha = 0, lambda = 0.01),
             trControl = trainControl(method = "none")),
  
  rf  = list(ntree = 150),
  
  svm = list(kernel = "radial",   # <-- added
             cost = 1,
             gamma = 0.1)
)

results <- benchmark(models, X, y, cv = 5, params = params)

## 
## [1/3] Fitting model: glm
## Mean CV score for glm: 0.9533
## 
## [2/3] Fitting model: rf
## Mean CV score for rf: 0.9600
## 
## [3/3] Fitting model: svm
## Mean CV score for svm: 0.9733

print(results)

## $glm
## $glm$avg_score
## [1] 0.9533333
## 
## $glm$scores
##     fold1     fold2     fold3     fold4     fold5 
## 0.9333333 0.9666667 0.9333333 0.9333333 1.0000000 
## 
## 
## $rf
## $rf$avg_score
## [1] 0.96
## 
## $rf$scores
##     fold1     fold2     fold3     fold4     fold5 
## 0.9333333 1.0000000 0.9333333 0.9333333 1.0000000 
## 
## 
## $svm
## $svm$avg_score
## [1] 0.9733333
## 
## $svm$scores
##     fold1     fold2     fold3     fold4     fold5 
## 0.9666667 1.0000000 0.9666667 0.9333333 1.0000000

Regression

library(unifiedml)
library(randomForest)
library(e1071)
library(caret)

set.seed(123)

# Regression data
X <- mtcars[, setdiff(names(mtcars), "mpg")]
y <- mtcars$mpg

models <- list(
  glm  = Model$new(caret::train),
  rf   = Model$new(randomForest::randomForest),
  svm  = Model$new(e1071::svm)
)

params <- list(
  glm = list(method = "glmnet",
             tuneGrid = data.frame(alpha = 0, lambda = 0.01),
             trControl = trainControl(method = "none")),
  
  rf  = list(ntree = 150),
  
  svm = list(type = "eps-regression",  # <-- important for regression
             kernel = "radial",
             cost = 1,
             gamma = 0.1)
)

results <- benchmark(models, X, y, cv = 5, params = params)

## 
## [1/3] Fitting model: glm
## Mean CV score for glm: 2.8020
## 
## [2/3] Fitting model: rf
## Mean CV score for rf: 2.1512
## 
## [3/3] Fitting model: svm
## Mean CV score for svm: 3.6608

print(results)

## $glm
## $glm$avg_score
## [1] 2.801956
## 
## $glm$scores
##    fold1    fold2    fold3    fold4    fold5 
## 3.656903 3.503580 1.815958 1.422791 3.610545 
## 
## 
## $rf
## $rf$avg_score
## [1] 2.15118
## 
## $rf$scores
##    fold1    fold2    fold3    fold4    fold5 
## 3.034925 2.577693 1.710550 1.237986 2.194744 
## 
## 
## $svm
## $svm$avg_score
## [1] 3.660827
## 
## $svm$scores
##    fold1    fold2    fold3    fold4    fold5 
## 4.654566 3.565078 3.394040 2.102445 4.588007