ENB | R Documentation |
The data is about energy performance of buildings, containing eight input variables: relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution and two output variables: heating load (HL) and cooling load (CL) of residential buildings. The goal is to predict two real valued responses from eight input variables. It can also be used as a multi-class classification problem if the response is rounded to the nearest integer.
data("ENB")
A data frame with 768 observations on the following 10 variables.
X1
Relative Compactness
X2
Surface Area
X3
Wall Area
X4
Roof Area
X5
Overall Height
X6
Orientation
X7
Glazing Area
X8
Glazing Area Distribution
Y1
Heating Load
Y2
Cooling Load
UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Energy+efficiency.
A. Tsanas, A. Xifara: 'Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools', Energy and Buildings, Vol. 49, pp. 560-567, 2012
data(ENB)
set.seed(1)
idx = sample(1:nrow(ENB), floor(nrow(ENB)*0.8))
train = ENB[idx, ]
test = ENB[-idx, ]
htt_enb = HTT(cbind(Y1, Y2) ~ . , data = train, controls = htt_control(pt = 0.05, R = 99))
# prediction
pred = predict(htt_enb, newdata = test)
test_y = test[, 9:10]
# MAE
colMeans(abs(pred - test_y))
# MSE
colMeans(abs(pred - test_y)^2)
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