Description Usage Arguments Value Meta Information References See Also Examples

Computes the weighted balanced accuracy, suitable for imbalanced data sets. It is defined analogously to the definition in sklearn.

First, the sample weights *w* are normalized per class:

*
w_hat[i] = w[i] / sum((t == t[i]) * w[i]).
*

The balanced accuracy is calculated as

*
1 / sum(w_hat) * sum((r == t) * w_hat).
*

1 |

`truth` |
:: |

`response` |
:: |

`sample_weights` |
:: |

`...` |
:: |

Performance value as `numeric(1)`

.

Type:

`"classif"`

Range:

*[0, 1]*Minimize:

`FALSE`

Required prediction:

`response`

brodersen_2010

guyon_2015

Other Classification Measures: `acc`

,
`ce`

, `logloss`

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