code: def classification_metrics(y_true, y_prob, threshold=0.5):
      y_pred = np.where(y_prob > threshold, 1, 0)
      metrics = {
          "Accuracy": sklm.accuracy_score,
          "Log_Loss": lambda real, pred: sklm.log_loss(
              real, y_prob, eps=1e-5
          ),
          # yes, I'm using y_prob here!
          "AUC": lambda real, pred: sklm.roc_auc_score(real, y_prob),
          "Precision": sklm.precision_score,
          "Recall": sklm.recall_score,
      }
      return evaluate_metrics(y_true, y_pred, metrics)
  
file_path: hummingbird/benchmarks/trees/metrics.py
imports: import numpy
  import sklearn.metrics
  from benchmarks.datasets import LearningTask
label_desc: Use image node_id 3 for calling a global function with example usage: classification_metrics(y_true, y_prob, threshold) and returns: evaluate_metrics