rvw 0.6.0: First release
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Note: Crossposted by Ivan, James and myself.
- A reworked interface that simplifies model manipulations (direct usage of CLI arguments is also available)
- Support of the majority of Vowpal Wabbit learning algorithms and reductions
- Extended
data.frameconverter covering different variations of Vowpal Wabbit input formats
library(rvw)
library(mlbench) # for a dataset
# Basic data preparation
data("BreastCancer", package = "mlbench")
data_full <- BreastCancer
ind_train <- sample(1:nrow(data_full), 0.8*nrow(data_full))
data_full <- data_full[,-1]
data_full$Class <- ifelse(data_full$Class == "malignant", 1, -1)
data_train <- data_full[ind_train,]
data_test <- data_full[-ind_train,]
# Simple Vowpal Wabbit model for binary classification
vwmodel <- vwsetup(dir = "./",
model = "mdl.vw",
option = "binary")
# Training
vwtrain(vwmodel = test_vwmodel,
data = data_train,
passes = 10,
targets = "Class")
# And testing
vw_output <- vwtest(vwmodel = test_vwmodel, data = data_test)
libvw and so initially we offer a Docker container in order to ship the most up to date package with everything needed.
This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.
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