auc

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 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 have a binary classification problem where I use the following code to get my weighted avarege precision , weighted avarege recall , weighted avarege f-measure and roc_auc . df = pd.read_csv(input_path+input_file) X = df[features] y = df[["gold_standard"]] clf = RandomForestClassifier(random_state = 42, class_weight="balanced") k_fold = StratifiedKFold(n_splits=10, shuffle=True, random_state=0) scores = cross_validate(clf, X, y, cv=k_fold, scoring = ('accuracy', 'precision_weighted',

来源： https://stackoverflow.com/questions/40555916/r-auc-of-multiple-samples-in-time-series

来源： https://stackoverflow.com/questions/40555916/r-auc-of-multiple-samples-in-time-series

来源： https://stackoverflow.com/questions/44004926/partial-fit-with-sgdclassifier-gives-fluctuating-accuracy

来源： https://stackoverflow.com/questions/44004926/partial-fit-with-sgdclassifier-gives-fluctuating-accuracy

来源： https://stackoverflow.com/questions/39174005/faster-auc-in-sklearn-or-python