This parameter is required for multiclass/multilabel targets. If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: ``'binary'``: Only repor
multiclass_msrs = function(cm) { # cm为table格式的多分类混淆矩阵 # 返回两个数据框分别存放单独度量和总体度量 m1 = tibble(Class = dimnames(cm)$truth, TP = diag(cm)) |> mutate(sumFN = colSums(cm) - TP, sumFP = rowSums(cm) - TP, Precision = TP / (TP + sumFP), Recall = TP...
F-beta score of the positive class in binary classification or weighted average of the F-beta score of each class for the multiclass task. 参考信息 Accuracy、Recall、Error Rate 和 Precision 详解(手写笔记) F-score - Wikipedia sklearn.metrics.fbeta_score — scikit-learn 1.4.1 documentation ...
sk_result = sklearn_proc(y_true, y_pred) check(spu_result, sk_result) # Test multiclass y_true = jnp.array([0, 1, 1, 0, 2, 1]) y_pred = jnp.array([0, 0, 1, 0, 2, 1]) spu_result = spsim.sim_jax(sim, proc, static_argnums=(2, 5))( y_true, y_pred, None,...
By the subsampling issue do you mean landing up with a binary sample from a multiclass problem, or a single label from a binary problem? Yes: a label will only be ignored if it is explicitly excluded from a list of labels; if the single label is retained according to labels, output wi...