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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