caretS3 and ahead for Tabular and Time Series Machine Learning
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Packages
#install.packages("remotes")
#install.packages("glmnet")
#install.packages("randomForest")
remotes::install_github("thierrymoudiki/caretS3")
caretS3::caretS3 Tabular Machine Learning
data(mtcars)
# Prepare data
X_reg <- mtcars[, -1] # All except mpg
y_reg <- mtcars$mpg # Target variable
# Split into train/test
set.seed(123)
idx_reg <- sample(nrow(X_reg), 0.7 * nrow(X_reg))
X_train <- X_reg[idx_reg, ]
y_train <- y_reg[idx_reg]
X_test <- X_reg[-idx_reg, ]
y_test <- y_reg[-idx_reg]
mod <- caretS3::caretS3(X_train, y_train, method = "glmnet")
Registered S3 method overwritten by 'future':
method from
all.equal.connection parallelly
Mazda RX4 Mazda RX4 Wag Hornet 4 Drive Valiant
22.88234 22.41295 21.12871 20.55533
Merc 450SE Merc 450SL Lincoln Continental Toyota Corona
14.37278 15.58185 10.76004 24.86612
Camaro Z28 Pontiac Firebird
13.55088 15.78322 Mazda RX4 Mazda RX4 Wag Hornet 4 Drive Valiant
20.09445 20.05342 19.60069 19.34515
Merc 450SE Merc 450SL Lincoln Continental Toyota Corona
15.35807 15.32211 12.98877 24.25831
Camaro Z28 Pontiac Firebird
15.33895 16.01282
ahead::dynrmf time series forecasting
par(mfrow=c(2, 2)) plot(ahead::dynrmf(AirPassengers, h=20, fit_func = caretS3::caretS3, fit_params = list(method="glmnet", alpha=0, lambda=0.1))) plot(ahead::dynrmf(AirPassengers, h=20, fit_func = caretS3::caretS3, fit_params = list(method="glmnet", alpha=0.5, lambda=0.1))) plot(ahead::dynrmf(AirPassengers, h=20, fit_func = caretS3::caretS3, fit_params = list(method="glmnet", alpha=1, lambda=0.1))) plot(ahead::dynrmf(AirPassengers, h=20, fit_func = caretS3::caretS3, fit_params = list(method="glm")))
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