Diagonal Example Dataset - DiRo2C

The two datasets are used to simulate two different black boxes that are supposed to predict different results in certain data areas.

Dataset diro2c_diagonal_example_dataset.csv:

Two-dimensional dataset with the continuous features x1 and x2. It contains 300 instances with the following properties: min = -126.19, max = 362.16, µ = 139.14, and σ = 77.79 for feature x1 and min = -429.45, max = 265.10, µ = 9.98, and σ = 157.80 for feature x2.

The instances of the datasets are classified into two classes “0” and “1”. The instances are generated by the sklearn “make_classification” function with the following parameters: make_classification(n_samples = 300, n_features = 2, n_informative = 2, n_redundant = 0, n_clusters_per_class = 1, class_sep = 1.3, random_state = 0, flip_y = 0, scale = 100).

Dataset diro2c_diagonal_example_manipulated_dataset.csv:

This dataset is manipulated on the basis of the first dataset as follows:

 (x1 < 100 : y = 0), (x1 >= 100 : y = 1)