ts_rf: Random Forest

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Random Forest

Description

Creates a time series prediction object that uses the Random Forest. It wraps the randomForest library.

Usage

ts_rf(preprocess = NA, input_size = NA, nodesize = 1, ntree = 10, mtry = NULL)

Arguments

preprocess	normalization
input_size	input size for machine learning model
nodesize	node size
ntree	number of trees
mtry	number of attributes to build tree

Value

returns a ts_rf object.

Examples

data(sin_data)
ts <- ts_data(sin_data$y, 10)
ts_head(ts, 3)

samp <- ts_sample(ts, test_size = 5)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

model <- ts_rf(ts_norm_gminmax(), input_size=4, nodesize=3, ntree=50)
model <- fit(model, x=io_train$input, y=io_train$output)

prediction <- predict(model, x=io_test$input[1,], steps_ahead=5)
prediction <- as.vector(prediction)
output <- as.vector(io_test$output)

ev_test <- evaluate(model, output, prediction)
ev_test

daltoolbox documentation built on Nov. 3, 2024, 9:06 a.m.