Gaussian Quantiles Datasets - DiRo2C

<p>The two datasets are used to simulate two different black boxes that are supposed to predict different results in certain data areas.</p> <p>Dataset diro2c_gaussian_dataset.csv:</p> <p>Two-dimensional dataset with the continuous features x1 and x2. It was created with &micro; = 0 and &sigma; 2 = 0.8. It contains 300 instances (data points) with the following properties for feature x1: min = &minus; 275.71, max = 255.90, &micro; = 0.04, and &sigma; = 88.54 and with the following properties for feature x2: min = &minus; 252.57, max = 201.03, &micro; = &minus; 9.44, and &sigma; = 86.20. The instances of the datasets are classified into two classes &ldquo;0&rdquo; and &ldquo;1&rdquo;. 150&nbsp;are assigned to the class &ldquo;0&rdquo;&nbsp;and 150 instances are assigned to the class &ldquo;1&rdquo;.&nbsp;The instances of the dataset&nbsp;are generated by the sklearn &ldquo;make_gaussian_quantiles&rdquo; function with the following parameters: make_gaussian_quantiles(n_samples = 300, n_classes = 2, shuffle = False, cov = 0.8, random_state = 7). Afterward, we scale the instances by the factor 100.</p> <p>Dataset diro2c_gaussian_manipulated_dataset.csv:</p> <p>Two-dimensional dataset with the continuous features x1 and x2. The manipulated dataset is generated with &micro; = 0 and &sigma; 2 = 1.3.&nbsp;It contains 300 instances (datapoints) with the following properties for feature x1: min = &minus; 351.46, max = 326.21, &micro; = 0.04, and &sigma; = 112.87 and with the following properties for feature x2: min = &minus; 321.97, max = 256.27, &micro; = &minus; 12.04, and &sigma; = 109.89. The instances of the datasets are classified into two classes &ldquo;0&rdquo; and &ldquo;1&rdquo;. 150 instances&nbsp;are assigned to the class &ldquo;0&rdquo;, and 150 instances are assigned to the class &ldquo;1&rdquo;.</p>