代码语言:javascript 代码运行次数:0 复制 Cloud Studio代码运行 # Set up controlfunctionfortraining ctrlprSummary<-trainControl(method="repeatedcv",number=10,repeats=5,summaryFunction=prSummary,classProbs=TRUE)# Build a standard classifier using a gradient boosted machine set.seed(5627)orig_fit2<-trai...
summaryFunction = prSummary, classProbs = TRUE) # Build a standard classifier using a gradient boosted machine set.seed(5627) orig_fit2 <- train(Class ~ ., data = imbal_train, method = "gbm", verbose = FALSE, metric = "AUC", trControl = ctrlprSummary) # Use the same seed to e...
对于不平衡数据集,AUC值是分类器效果评估的常用标准。但如果在解释时不仔细,它也会有一些误导。以Davis and Goadrich (2006)中的模型为例。如图所示,左侧展示的是两个模型的ROC曲线,右侧展示的是precision-recall曲线 (PRC)。 Precision值和Recall值是既矛盾又统一的两个指标,为了提高Precision值,分类器需要尽量在...
zeros_like(probs_neg), np.ones_like(probs_pos))) fpr, tpr, _ = roc_curve(labels, probs) auc_score = auc(fpr, tpr) if plot: plt.figure(figsize=(7, 6)) plt.plot(fpr, tpr, color='blue', label='ROC (AUC = %0.4f)' % auc_score) plt.legend(loc='lower right') plt....
## 1 -Inf 0 1 ## 2 0.03 0 1 ## 3 0.04 0 0.976 ## 4 0.05 0.0694 0.976 ## 5 0.06 0.111 0.976 ## 6 0.07 0.139 0.976 ## 7 0.08 0.222 0.902 ## 8 0.09 0.306 0.878 ## 9 0.1 0.389 0.829 ## 10 0.11 0.486 0.780 ## # ℹ 42 more rows ...
please avoid AUCall – it's Pharsight's invention. You will not find it in any textbook on PK. Regulatory authorities worldwide require AUCt(=AUClast) and AUC∞(=AUCinf). If you go with AUCinf, I would recommend to use AUCinfpred instead of AUCinfobs. ...
Rpart cp = 0, depth = 9) 0.965 (∓0.005) 0.722 (∓0.077) 0.613 (∓0.078) 0.916 (∓0.024) 0.441 (∓0.118) 0.151 (∓0.192) 27.8 (∓6.268) 2 (∓0.000) Iris data ImbTreeAUC (cp = 0.01, depth = 3) 0.976 (∓0.006) 0.987 (∓0.006) 0.963 (∓0.009) 0.960 (...
AUCINF_obs代表从给药开始到理论外推无穷远的时间的AUC (计算=AUClast+Clast/Lambda_ z ),AUCINF_obs计算中的Clast是指实际测量的Clast(最后一个可以测量到的浓度); AUCINF_ pred代表从给药开始到理论外推无穷远的时间的AUC,AUCINF_ pred计算中的Clast是指用线性回归计算的最后一点浓度(比如,你的最后一个观...
## 1 -Inf 0 1 ## 2 0.03 0 1 ## 3 0.04 0 0.976 ## 4 0.05 0.0694 0.976 ## 5 0.06 0.111 0.976 ## 6 0.07 0.139 0.976 ## 7 0.08 0.222 0.902 ## 8 0.09 0.306 0.878 ## 9 0.1 0.389 0.829 ## 10 0.11 0.486 0.780 ## # ℹ 42 more rows ...