萨默维尔幸福调查数据集|居民满意度数据集|情感分析数据集

Waldemar W. Koczkodaj, wkoczkodaj@gmail, independent researcher. Data Set Information: It is a case of supervised learning with the use of Receiver Operating Characteristic (ROC) to select the minimal set of attributes preserving or increasing predictability of the data. Attribute Information: D = decision attribute (D) with values 0 (unhappy) and 1 (happy) X1 = the availability of information about the city services X2 = the cost of housing X3 = the overall quality of public schools X4 = your trust in the local police X5 = the maintenance of streets and sidewalks X6 = the availability of social community events Attributes X1 to X6 have values 1 to 5. Relevant Papers: Koczkodaj, W.W.; Li, F.; Wolny-Dominiak, A., RatingScaleReduction package: stepwise rating scale item reduction without predictability loss, R JOURNAL, 10(1): 43-55, 2018. W.W. Koczkodaj, T. Kakiashvili, A. Szymanska, J. Montero-Marin, R. Araya, J. Garcia-Campayo, K. Rutkowski, D. Strzalka, How to reduce the number of rating scale items without predictability loss? Scientometrics, 111(2): 581-593, 2017. Project R package: [Web link] Citation Request: For the method: W.W. Koczkodaj, T. Kakiashvili, A. Szymanska, J. Montero-Marin, R. Araya, J. Garcia-Campayo, K. Rutkowski, D. Strzalka, How to reduce the number of rating scale items without predictability loss? Scientometrics, 111(2): 581-593, 2017.