roc

Using the R package caret, how can I generate a ROC curve based on the cross-validation results of the train() function? Say, I do the following: data(Sonar) ctrl <- trainControl(method="cv", summaryFunction=twoClassSummary, classProbs=T) rfFit <- train(Class ~ ., data=Sonar, method="rf", preProc=c("center", "scale"), trControl=ctrl) The training function goes over a range of mtry parameter and calculates the ROC AUC. I would like to see the associated ROC curve -- how do I do that? Note: if the method used for sampling is LOOCV, then rfFit will contain a non-null data frame in the rfFit$pred

When I use multiclass.roc function in R (pROC package), for instance, I trained a data set by random forest, here is my code: # randomForest & pROC packages should be installed: # install.packages(c('randomForest', 'pROC')) data(iris) library(randomForest) library(pROC) set.seed(1000) # 3-class in response variable rf = randomForest(Species~., data = iris, ntree = 100) # predict(.., type = 'prob') returns a probability matrix multiclass.roc(iris$Species, predict(rf, iris, type = 'prob')) And the result is: Call: multiclass.roc.default(response = iris$Species, predictor = predict(rf, iris, type

Can someone explain me please how to plot a ROC curve with ROCR. I know that I should first run: prediction(predictions, labels, label.ordering = NULL) and then: performance(prediction.obj, measure, x.measure="cutoff", ...) I am just not clear what is meant with prediction and labels. I created a model with ctree and cforest and I want the ROC curve for both of them to compare it in the end. In my case the class attribute is y_n, which I suppose should be used for the labels. But what about the predictions? Here are the steps of what I do (dataset name= bank_part): pred<-cforest(y_n~.,bank

I am trying to plot a ROC curve to evaluate the accuracy of a prediction model I developed in Python using logistic regression packages. I have computed the true positive rate as well as the false positive rate; however, I am unable to figure out how to plot these correctly using matplotlib and calculate the AUC value. How could I do that? uniquegino Here are two ways you may try, assuming your model is an sklearn predictor: import sklearn.metrics as metrics # calculate the fpr and tpr for all thresholds of the classification probs = model.predict_proba(X_test) preds = probs[:,1] fpr, tpr,

分类模型评估： 指标 描述 Scikit-learn函数 Precision 精准度 from sklearn.metrics import precision_score Recall 召回率 from sklearn.metrics import recall_score F1 F1值 from sklearn.metrics import f1_score Confusion Matrix 混淆矩阵 from sklearn.metrics import confusion_matrix ROC ROC曲线 from sklearn.metrics import roc AUC ROC曲线下的面积 from sklearn.metrics import auc 回归模型评估： 指标 描述 Scikit-learn函数 Mean Square Error (MSE, RMSE) 平均方差 from sklearn.metrics import mean_squared_error Absolute Error (MAE, RAE) 绝对误差 from sklearn.metrics import mean_absolute_error, median_absolute_error R-Squared R平方值 from sklearn.metrics import r2