Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
Table of contents
- 0 - Install packages + global parameters
- 1 - Regression example
- 2 - Classification example
0 - Install packages + global parameters
Let’s start by installing the main package, crossvalidation (version 0.5.0):
- 1st method: from R-universe (where you can also package’s long-form descriptions a.k.a vignettes)
In R console:
options(repos = c(
techtonique = 'https://techtonique.r-universe.dev',
CRAN = 'https://cloud.r-project.org'))
install.packages("crossvalidation")
- 2nd method: from Github
In R console:
remotes::install_github("Techtonique/crossvalidation")
When using this package, please note that I’m calling a “validation set”, what is usually called a “test set”. Because it makes more sense to me (even if I’m the only one in the world doing this).
Number of folds and repeats for the cross-validation procedure:
(n_folds <- 10)
(repeats <- 5)
Loading the other Statistical/Machine Learning packages needed for this post:
library(glmnet)
library(xgboost)
library(Matrix)
library(randomForest)
library(crossvalidation)
1 - Regression example
# dataset
set.seed(123)
n <- 100 ; p <- 5
X <- matrix(rnorm(n * p), n, p)
print(head(X))
y <- rnorm(n)
print(head(y))
least squares
# linear model example
(cv_lm <- crossvalidation::crossval_ml(x = X, y = y, k = n_folds,
repeats = repeats, show_progress = FALSE))
glmnet
# glmnet example -----
# fit glmnet, with alpha = 1, lambda = 0.1
(cv_glmnet <- crossvalidation::crossval_ml(x = X, y = y, k = n_folds,
repeats = repeats,
show_progress = FALSE,
fit_func = glmnet, predict_func = predict,
packages = c("glmnet", "Matrix"),
fit_params = list(alpha = 0, lambda = 0.01)))
Random Forest
# randomForest example -----
# fit randomForest with mtry = 4
(
cv_rf <- crossvalidation::crossval_ml(
x = X,
y = y,
k = n_folds,
repeats = repeats,
show_progress = FALSE,
fit_func = randomForest::randomForest,
predict_func = predict,
packages = "randomForest",
fit_params = list(mtry = 4)
)
)
xgboost
# xgboost example -----
# The response and covariates are named 'label' and 'data'
# So, we do this:
f_xgboost <- function(x, y, ...) xgboost::xgboost(data = x, label = y, ...)
# fit xgboost with nrounds = 10
(
cv_xgboost <-
crossvalidation::crossval_ml(
x = X,
y = y,
k = n_folds,
repeats = repeats,
show_progress = FALSE,
fit_func = f_xgboost,
predict_func = predict,
#packages = "xgboost",
fit_params = list(nrounds = 10,
verbose = FALSE)
)
)
glmnet
# glmnet example -----
# fit glmnet, with alpha = 0.5, lambda = 0.1
cv_glmnet1 <- crossvalidation::crossval_ml(x = X, y = y, k = n_folds,
repeats = repeats,
show_progress = FALSE,
fit_func = glmnet,
predict_func = predict.glmnet,
packages = c("glmnet", "Matrix"),
fit_params = list(alpha = 0.5,
lambda = 0.1,
family = "gaussian"))
# fit glmnet, with alpha = 0, lambda = 0.01
cv_glmnet2 <- crossvalidation::crossval_ml(x = X, y = y, k = n_folds, repeats = repeats, show_progress = FALSE,
fit_func = glmnet::glmnet, predict_func = predict.glmnet,
packages = c("glmnet", "Matrix"), fit_params = list(alpha = 0, lambda = 0.01, family = "gaussian"))
# fit glmnet, with alpha = 0, lambda = 0.01
cv_glmnet3 <- crossvalidation::crossval_ml(x = X, y = y, k = n_folds, repeats = repeats, show_progress = FALSE,
fit_func = glmnet::glmnet, predict_func = predict.glmnet,
packages = c("glmnet", "Matrix"), fit_params = list(alpha = 0, lambda = 0.01))
boxplots for regression
(samples <- crossvalidation::create_samples(cv_lm, cv_glmnet1,
cv_glmnet2, cv_glmnet3,
cv_rf, cv_xgboost,
model_names = c("lm", "glmnet1", "glmnet2",
"glmnet3", "rf", "xgb")))
boxplot(samples, main = "RMSE")
2 - Classification example
data(iris)
X <- as.matrix(iris[, 1:4])
print(head(X))
y <- factor(as.numeric(iris$Species))
print(head(y))
glmnet
# glmnet example -----
predict_glmnet <- function(object, newx) {
as.numeric(predict(object = object,
newx = newx,
type = "class"))
}
(cv_glmnet_1 <- crossvalidation::crossval_ml(x = X,
y = as.integer(iris$Species),
k = n_folds, repeats = repeats, show_progress = FALSE,
fit_func = glmnet, predict_func = predict_glmnet,
packages = c("glmnet", "Matrix"), fit_params = list(alpha = 0.5, lambda = 0.1, family = "multinomial"))) # better to use `nlambda`
(cv_glmnet_2 <- crossvalidation::crossval_ml(x = X,
y = as.integer(iris$Species),
k = n_folds, repeats = repeats, show_progress = FALSE,
fit_func = glmnet::glmnet, predict_func = predict_glmnet,
packages = c("glmnet", "Matrix"), fit_params = list(alpha = 0, lambda = 0.01, family = "multinomial")))
(cv_glmnet_3 <- crossvalidation::crossval_ml(x = X, y = as.integer(iris$Species) , k = n_folds, repeats = repeats, show_progress = FALSE,
fit_func = glmnet::glmnet, predict_func = predict_glmnet,
packages = c("glmnet", "Matrix"), fit_params = list(alpha = 1, lambda = 0.01, family = "multinomial")))
Random Forest
# randomForest example -----
# fit randomForest with mtry = 4
(
cv_rf <- crossvalidation::crossval_ml(
x = X,
y = y,
k = n_folds,
repeats = repeats,
show_progress = FALSE,
fit_func = randomForest::randomForest,
predict_func = predict,
#packages = "randomForest",
fit_params = list(mtry = 2L)
)
)
xgboost
y <- as.integer(iris$Species) - 1
print(y)
# xgboost example -----
# fit xgboost with nrounds = 10
f_xgboost <- function(x, y, ...) {
#xgb_train = xgb.DMatrix(data=x, label=y)
xgboost::xgboost(data = x, label = y, ...)
}
(cv_xgboost <- crossvalidation::crossval_ml(x = X, y = y, k = n_folds, repeats = repeats, fit_func = f_xgboost, predict_func = predict,
packages = "xgboost",
show_progress = FALSE,
fit_params = list(nrounds = 50L,
verbose = FALSE,
params = list(max_depth = 3L,
eta = 0.1,
subsample = 0.8,
colsample_bytree = 0.8,
objective = "multi:softmax",
num_class = 3L))))
boxplots for classification
(samples <- crossvalidation::create_samples(cv_rf, cv_glmnet_1,
cv_glmnet_2, cv_glmnet_3,
cv_xgboost,
model_names = c("rf", "glmnet1", "glmnet2",
"glmnet3", "xgb")))
boxplot(samples, main = "Accuracy")
abline(h = 1, col = "red", lty = 2, lwd = 2)
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