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Author: Angeliki Xifara, Athanasios Tsanas Source: UCI Please cite: Source: The dataset was created by Angeliki Xifara (angxifara @, Civil/Structural Engineer) and was processed by Athanasios Tsanas (tsanasthanasis @, Oxford Centre for Industrial and Applied Mathematics, University of Oxford, UK). Data Set Information: We perform energy analysis using 12 different building shapes simulated in Ecotect. The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valued responses. It can also be used as a multi-class classification problem if the response is rounded to the nearest integer. Attribute Information: The dataset contains eight attributes (or features, denoted by X1...X8) and two responses (or outcomes, denoted by y1 and y2). The aim is to use the eight features to predict each of the two responses. Specifically: 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

10 features

y1 (target)nominal37 unique values
0 missing
V1numeric12 unique values
0 missing
V2numeric12 unique values
0 missing
V3numeric7 unique values
0 missing
V4numeric4 unique values
0 missing
V5numeric2 unique values
0 missing
V6numeric4 unique values
0 missing
V7numeric4 unique values
0 missing
V8numeric6 unique values
0 missing
y2nominal38 unique values
0 missing

18 properties

Number of instances (rows) of the dataset.
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
Number of missing values in the dataset.
Number of instances with at least one value missing.
Number of numeric attributes.
Number of nominal attributes.
Percentage of binary attributes.
Percentage of instances having missing values.
Percentage of missing values.
The predictive accuracy obtained by always predicting the majority class.
Percentage of numeric attributes.
Number of attributes divided by the number of instances.
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Number of instances belonging to the least frequent class.
Number of binary attributes.

4 tasks

69 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: y2
69 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: y1
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