Accuracy_score:0.995565 Precision_score:0.98370536854472908 Recall_score:0.971350751234545 F1_score:0.977489 Auc:0.9887765786
时间: 2024-06-07 21:09:26 浏览: 16
这是一个分类模型的评估指标结果。Accuracy_score表示模型的准确率,Precision_score表示模型的精确率,Recall_score表示模型的召回率,F1_score是综合考虑了精确率和召回率的指标,Auc表示模型的曲线下面积。从这些指标的值来看,这个分类模型表现不错,准确率高,精确率和召回率也比较均衡。
相关问题
accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
这些都是常见的用于评估分类模型性能的指标。其中,accuracy_score 表示分类器正确预测样本的比例;precision_score 表示被分类器预测为正例的样本中真正是正例的比例;recall_score 表示真正是正例的样本中被分类器预测为正例的比例;f1_score 是 precision_score 和 recall_score 的调和平均数;roc_auc_score 是根据分类器的正例预测值排序后,计算ROC曲线下面积的指标。
Only one class present in y_true. ROC AUC score is not defined in that case.
ROC AUC score is a performance metric used to evaluate the performance of classification models. It is calculated based on the true positive rate (TPR) and false positive rate (FPR) at various threshold values. However, when there is only one class present in the true labels `y_true`, ROC AUC score is not defined because there is no distinction between positive and negative cases.
In such cases, you can use alternative performance metrics such as accuracy, precision, recall, or F1-score to evaluate your model's performance. These metrics can be easily calculated using scikit-learn's `metrics` module. Here's an example code snippet:
```
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
y_true = [0, 0, 0, 0] # Only one class present
y_pred = [0, 1, 0, 0] # Example predictions
acc = accuracy_score(y_true, y_pred)
prec = precision_score(y_true, y_pred)
rec = recall_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)
print("Accuracy: {:.2f}".format(acc))
print("Precision: {:.2f}".format(prec))
print("Recall: {:.2f}".format(rec))
print("F1-score: {:.2f}".format(f1))
```
This will output the accuracy, precision, recall, and F1-score of the model on the given predictions. Note that these metrics are only applicable when there is more than one class present in the true labels.
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