# Sparse Logistic Regression: Comparison of Regularization and Bayesian Implementations
## 5 Datasets
### Real Data  
Seven different real-world datasets are considered for RVM and LASSO performance assessment. Details of their key structural characteristics are given in Table 1 and Figure 1. With the exception of the semiconductor manufacturing and Fabric Care Appliance dataset, the datasets can be downloaded from the UCI Machine Learning Repository (source: [[http://archive.ics.uci.edu/ml](http://archive.ics.uci.edu/ml)]([http://archive.ics.uci.edu/ml](http://archive.ics.uci.edu/ml))). 1. **South Africa heart disease:** This dataset collects the results of an epidemiological study aimed at establishing the intensity of ischemic heart disease risk factors on the population in the high-incidence region of Western Cape, South Africa. The data represent white males between 15 and 64, and the response variable is the presence or absence of myocardial infarction (MI) at the time of the survey. Some labels, in this case, are for example systolic blood pressure or low-density lipoprotein cholesterol, obesity, etc. 2. **Abalone:** The age of abalone (a sea snail) can be determined from its shell measurements, such as shell weight, diameter, and others. In this dataset, the age is divided into two classes, namely older than or younger than 19 months. 3. **Semiconductor:** In the semiconductor manufacturing industry, virtual metrology refers to the use of models to predict \"costly to measure\" key physical variables from more accessible in-line measurements [52]. In this case, differently from the previous Section 5.1, we have a real target output (not built artificially): the plasma etch rate (ER) during the production of silicon wafers [53]. The in-line measurements are obtained from optical emission spectroscopy data consisting of specific wavelengths from which statistical moments (mean, variance, skewness, etc.) are calculated. We recast the prediction of ER into a classification problem with the aim of classifying out-of-spec ER measurements (outside reference values) and identify input variables that help to identify these events. In order to deal with the potentially high number of features extracted from the data (each statistical moment is calculated for each wavelength), only features presenting a reasonably good correlation with ER are used as candidate variables to build the models. This dataset presents high collinearity among subsets of variables as can be seen from Figure 2a. 4. **Arcene's task is to distinguish cancer versus normal patterns from mass-spectrometric data. This is a two-class classification problem with continuous input variables. 5. **Gisette** is a handwritten digit recognition problem focused on the separation of the highly confusible digits \"4\" and \"9\". Arcene, Gisette and Madelon dataset explained before are three of the five datasets used in the NIPS 2003 feature selection challenge. 6. **Fabric Care Appliance** is a dataset provided by Electrolux Italia S.p.a, fabric and dish care R&D. This dataset was collected with the purpose of equipping Electrolux household heat pump tumble dryers with an on-line model to predict the dry load weight of the laundry in the first part of the cycle. Generally speaking, the laundry weight of the loaded in the drum of a laundry treatment machine is an important piece of information; laundry weight can be used to set various washing/drying cycle parameters and to optimize performances and efficiency. Unfortunately, dedicated weight sensors cannot be included in consumer laundry equipment given the related costs. For this reason, a soft sensor approach, i.e., a black-box model, could be used to estimate laundry weight based on sensors already in place in laundry treatment equipment. The goal here is to distinguish between two sets of load classes: the output is a binary vector whose value is 0 for small load classes (i.e., classes I, II, III) and 1 for large load classes (i.e., classes IV, V, VI, VII); the input is a matrix of features (variables) derived from signals available on machines using expert knowledge.