P/R互为掣肘,若阈值极高,因此此时召回正例比较严格,必然Precision高,Recall低;若阈值极低,因此此时召回正例比较宽松,必然Recall高,Precision低。 多类别指标 macro avg 宏平均 对每个类别分别计算p/r/f后直接取平均 micro avg 微平均(用的极少) 不区分样本,计算整体的p/r/f weighted avg 加权平均 首先分别计...
micro avg 0.36 0.64 0.46 25 macro avg 0.38 0.62 0.47 25 weighted avg 0.39 0.64 0.48 25 samples avg 0.36 0.36 0.36 25 ''' 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. # 计算fpr和tpr from sklearn.metrics import roc_curve, auc fpr = dict(...
本文将介绍: 混淆矩阵(Confusion Matrix) 准确率(accuracy) 召回率(recall) 精确率(precision) F1score ROC和AUC 宏平均(macro avg) 微平均(micro avg) 加权平均(weighted avg) 一,混淆矩阵(Confusion Matrix) 在n分类模型中,使用n行n列的矩阵形式来表示精度,纵列代表n个分类,在每行中的... ...
2_52" target="_blank">其中average参数有五种:(None, ‘micro’, ‘macro’, ‘weighted’, ‘samples’) . 2、召回率 metrics.recall_score(y_true, y_pred, average='micro') Out[134]: 0.33333333333333331 metrics.recall_score(y_true, y_pred, average='macro') Out[135]: 0.3125 1. 2. 3....
y_pred = [1, 1, 2] y_true = [1, 1, 1] print(classification_report(y_true, y_pred, labels=[1, 2])) precision recall f1-score support 1 1.00 0.67 0.80 3 2 0.00 0.00 0.00 0 accuracy 0.67 3 macro avg 0.50 0.33 0.40 3 weighted avg 1.00 0.67 0.80 3 例子2 y_pred = [1...
'''Trains a simple convnet on the MNIST dataset. Gets to 99.25% test accuracy after 12 epochs (there is still a lot of margin for parameter tuning). 16 seconds per epoch on a GRID K520 GPU. ''' #from __future__ import print_function import numpy as np np.random.seed(1337) # ...
RANK.AVG RANK.EQ STDEV.P STDEV.S T.DIST T.DIST.2T T.DIST.RT T.DIST.RT T.INV T.INV.2T T.INV.RT T.TEST VAR.P VAR.S WEEKDAY WEEKNUM WEIBULL.DIST WORKDAY.INTL Z.TEST Applies to 產品版本 Excel primary interop assembly Latest 意見...
17.1s 11 macro avg 0.83 0.74 0.77 9769 17.1s 12 weighted avg 0.85 0.85 0.84 9769 17.1s 13 20.4s 14 [NbConvertApp] Converting notebook __notebook__.ipynb to notebook 20.5s 15 [NbConvertApp] Writing 27546 bytes to __notebook__.ipynb 21.3s 16 [NbConvertApp] Converting notebook ...
I found, the precision and recall value given by the caret package in R are different from the actual definition of them inhttps://en.wikipedia.org/wiki/Precision_and_recall. Could you tell me why it is? In fact , I got an online confusion matrix where both results are showing.http:...
Evaluation Matrix precision recall f1-score support 1 0.99 0.98 0.99 367 2 0.91 0.96 0.93 502 3 0.90 0.86 0.88 523 4 0.84 0.92 0.88 517 5 0.90 0.84 0.87 527 6 0.93 0.90 0.91 396 accuracy 0.91 2832 macro avg 0.91 0.91 0.91 2832 weighted avg 0.91 0.91 0.91 2832 About...