Classification metrics can't handle a mix of multiclass-multioutput and binary targets

This error message typically occurs when you try to evaluate a model that has both multiclass-multioutput and binary targets using a classification metric.

Classification metrics such as accuracy, precision, recall, and F1-score are designed to evaluate models that have single-output binary or multiclass targets. When the targets are multiclass-multioutput, meaning that each sample has more than one target label, the classification metrics cannot be applied directly.

To resolve this issue, you can use evaluation metrics that are designed for multiclass-multioutput problems, such as Hamming loss, Jaccard similarity score, and zero-one loss. Another approach would be to transform the multiclass-multioutput targets into a single-output format, for example, by using binary relevance or label powerset methods. This will allow you to use standard classification metrics for evaluation.