Simulated training and test set for imbalanced binary classification. The rare class may be described as a half circle depleted filled with the prevalent class, which is normally distributed and has elliptical contours.

1 | ```
data(hacide)
``` |

Data represent 2 real features (denoted as `x1, x2`

) and a binary label class (denoted as `cls`

). Positive examples occur in about 2% of the data.

`hacide.train`

Includes 1000 rows and 20 positive examples.

`hacide.test`

Includes 250 rows and 5 positive examples.

Data have been simulated as follows:

- -
if

`cls`

= 0 then`(x1, x2)`

*\sim \mathbf{N}_{2} ≤ft(\mathbf{0}_{2}, (1/4, 1) \mathbf{I}_{2}\right)*- -
if

`cls`

= 1 then`(x1, x2)`

*\sim \mathbf{N}_{2} ≤ft(\mathbf{0}_{2}, \mathbf{I}_{2}\right) \cap ≤ft\|\mathbf{x}\right\|^2>4 \cap x_2 ≤q 0*

Lunardon, N., Menardi, G., and Torelli, N. (2014). ROSE: a Package for Binary Imbalanced Learning. *R Jorunal*, 6:82–92.

Menardi, G. and Torelli, N. (2014). Training and assessing classification rules with imbalanced data. *Data Mining and Knowledge Discovery*, 28:92–122.

1 2 3 | ```
data(hacide)
summary(hacide.train)
summary(hacide.test)
``` |

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