# A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability
## 5 Description of Datasets  
In order to evaluate the efficiency and the flexibility of the proposed Grey-Box model, we utilized in our experiments several benchmark datasets on various real-world application domains. These datasets are two educational datasets by a private school, two financial datasets (Australian credit and Bank marketing acquired from UCI Machine Learning Repository) and two medical datasets (Coimbra and Breast Cancer Wisconsin). A brief description of the datasets characteristics and structure is presented in Table 1.  
The educational datasets were collected by a Microsoft showcase school during the years 2007-2016 concerning the performance of 2260 students in courses of \"algebra\" and \"geometry\" of the first two years of Lyceum [22]. The attributes of this dataset have numerical values ranging from 0 (lowest grade) to 20 (highest grade) referring to the students' performance on the 1 st and 2 nd semester and the dependent variable of this dataset has four possible states (\"fail\", \"good\", \"very good\", \"excellent\") indicating the students' grade in the final examinations. Since it is of high importance for an educator to recognize weak students in the middle of the academic period, two datasets have been created, namely EduDataA and EduDataAB. EduDataA contains the attributes concerning the  
Figure 2: Grey-Box model flow chart.  
students' performance during the 1 st semester. EduDataAB contains the attributes concerning the students' performance during the 1 st and 2 nd semesters.  
The Australian credit dataset [22] concerns approved or rejected credit card applications, containing 690 instances and 14 attributes (six numerical and eight categorical). An interesting thing with this dataset is the variation and the mixture of attributes which is continuous, nominal with small numbers of values and nominal with larger numbers of values. The Bank Marketing dataset [23] is associated with direct marketing campaigns (phone calls) of a Portuguese banking institution. The goal is to identify if the client will subscribe a term deposit (target value y). For the bank marketing staff is very important to know potential clients for subscribing a term deposit since they could decide on which customer to call and which one to leave alone without bothering them with irrelevant product calls. The Bank Marketing dataset consists of 4119 instances and 20 features.  
The Coimbra dataset [24] is comprised by ten attributes, all quantitative and a binary dependent variable which indicates the presence or absence of breast cancer. All clinical features were measured for 64 patients with breast cancer and 52 healthy controls. The developing of accurate prediction models based on these ten attributes can potentially be used as a biomarker of breast cancer. The Breast Cancer Wisconsin dataset [22] is comprised by 569 patient instances and 32 attributes. The features were computed from a digitized image of a fine needle aspirate of a breast mass. They describe characteristics of the cell nuclei present in the image. The output attribute is the patient's diagnosis (malignant or benign).