roc

问题 I have a data having 3 class labels(0,1,2). I tried to make ROC curve. and did it by using pos_label parameter. fpr, tpr, thresholds = metrics.roc_curve(Ytest, y_pred_prob, pos_label = 0) By changing pos_label to 0,1,2- I get 3 graphs, Now I am having issue in calculating AUC score. How can I average the 3 graphs and plot 1 graph from it and then calculate the Roc_AUC score. i am having error in by this metrics.roc_auc_score(Ytest, y_pred_prob) ValueError: multiclass format is not supported

问题 I have a neural network model and I am using KerasClassifier and then using KFold for cross-validation. Now I am having issues in plotting the ROC curve. I have tried few codes but most of them is giving me an error of multi-labeled is not interpreted. I have the following code till my neural network produces the accuracy. I will be thankful if anyone can help me with the later part of the code. import numpy as np import pandas as pd from keras.layers import Dense, Input from keras.models

问题 I have a neural network model and I am using KerasClassifier and then using KFold for cross-validation. Now I am having issues in plotting the ROC curve. I have tried few codes but most of them is giving me an error of multi-labeled is not interpreted. I have the following code till my neural network produces the accuracy. I will be thankful if anyone can help me with the later part of the code. import numpy as np import pandas as pd from keras.layers import Dense, Input from keras.models

问题 I am not able to get ROC function to work, I get the error "Predictor must be numeric or ordered". I've looked through other posts, but nothing solves my problem. Any help is highly appreciated. "Get data" flying=dget("https://www.math.ntnu.no/emner/TMA4268/2019v/data/flying.dd") ctrain=flying$ctrain ctest=flying$ctest library(MASS) fly_qda=qda(diabetes~., data=ctrain) #Test error is given below: predict_qda=predict(fly_qda, newdata=ctest, probability=TRUE) table_qda<-table(ctest$diabetes,

问题 I am trying to use the scikit-learn module to compute AUC and plot ROC curves for the output of three different classifiers to compare their performance. I am very new to this topic, and I am struggling to understand how the data I have should input to the roc_curve and auc functions. For each item within the testing set, I have the true value and the output of each of the three classifiers. The classes are ['N', 'L', 'W', 'T'] . In addition, I have a confidence score for each value output

问题 I am following the documentation for plotting ROC curves for multiple classes at this link: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html I am confused about this line in particular: y_score = classifier.fit(X_train, y_train).decision_function(X_test) I've seen that in other examples, y_score holds probabilities, and they are all positive values, as we would expect. However, the y_score (each column for classes A-C) in this example has mostly negative values.

问题 I have a dataset with 6 classes and I would like to plot a ROC curve for a multiclass classification. The first answer in this thread given by Achim Zeileis is a very good one. ROC curve in R using rpart package? But this works only for a binomial classification. And the error i get is Error in prediction, Number of classes is not equal to 2 . Any one who has done this for a multi-class classification? Here is a simple example of what I am trying to do. data <- read.csv("colors.csv") let's

问题 I want to evaluate my classification models with a ROC curve. I'm struggling with computing a multiclass ROC Curve for a cross-validated data set. There is no division in train and test set, because of the cross-validation. Underneath, you can see the code I already tried. scaler = StandardScaler(with_mean=False) enc = LabelEncoder() y = enc.fit_transform(labels) vec = DictVectorizer() feat_sel = SelectKBest(mutual_info_classif, k=200) n_classes = 3 # Pipeline for computing of ROC curves clf

问题 I want to evaluate my classification models with a ROC curve. I'm struggling with computing a multiclass ROC Curve for a cross-validated data set. There is no division in train and test set, because of the cross-validation. Underneath, you can see the code I already tried. scaler = StandardScaler(with_mean=False) enc = LabelEncoder() y = enc.fit_transform(labels) vec = DictVectorizer() feat_sel = SelectKBest(mutual_info_classif, k=200) n_classes = 3 # Pipeline for computing of ROC curves clf

问题 I would like to calculate the 10-fold cross-validated AUC of an elastic net regression model with the optimal alpha and lambda using caret::train https://stats.stackexchange.com/questions/69638/does-caret-train-function-for-glmnet-cross-validate-for-both-alpha-and-lambda/69651 explains how to cross-validate alpha and lambda with caret::train My question on Cross Validated got closed, because it has been classified as a programming question: https://stats.stackexchange.com/questions/505865/r