Description Usage Arguments Details Value

`generate_imbalanced_data`

is a simple function to generate a
two-class imbalanced data set.

1 2 | ```
generate_imbalanced_data(num_examples = 100L, num_features = 2L,
imbalance_ratio = 5, noise_maj = 0.05, noise_min = 0.1, seed = NULL)
``` |

`num_examples` |
Total number of examples in the data set. |

`num_features` |
Total number of features in the data set. |

`imbalance_ratio` |
Ratio of the number of examples in the majority class to the number of examples in the minority class. |

`noise_maj` |
Fraction of the minority class that is mislabelled as majority class. |

`noise_min` |
Fraction of the majority class that is mislabelled as minority class. |

`seed` |
Integer value for reproducibility purposes. |

The imbalanced data set generated has two classes where the majority class comes from a multivariate normal distribution with mean zero and unitary standard deviation for all features and the minority class comes from a multivariate normal distribution with mean two and unitary standard deviation for all features.

The total number of examples and the dimensionality of the data are chosen
through the `num_examples`

and `num_features`

arguments. The
`imbalance_ratio`

argument together with `num_examples`

determines the exact number of examples in the majority and minority
classes. To simulate noise in the data, approximately `noise_min`

examples in the majority class are labelled as minority class examples and
approximately `noise_maj`

examples in the minority class are labelled
as majority class examples. `noise_maj`

and `noise_min`

are
fractions.

A data frame containing an imbalanced two-class data set.

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