roc

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I have difficulty in plotting OneClassSVM's AUC plot in python (I am using sklearn which generates confusion matrix like [[tp, fp],[fn,tn]] with fn=tn=0 . from sklearn.metrics import roc_curve, auc fpr, tpr, thresholds = roc_curve(y_test, y_nb_predicted) roc_auc = auc(fpr, tpr) # this generates ValueError[1] print "Area under the ROC curve : %f" % roc_auc plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc) I want to handle error [1] and plot AUC for OneClassSVM . [1] ValueError: Input contains NaN, infinity or a value too large

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I'm doing different text classification experiments. Now I need to calculate the AUC-ROC for each task. For the binary classifications, I already made it work with this code: scaler = StandardScaler(with_mean=False) enc = LabelEncoder() y = enc.fit_transform(labels) feat_sel = SelectKBest(mutual_info_classif, k=200) clf = linear_model.LogisticRegression() pipe = Pipeline([('vectorizer', DictVectorizer()), ('scaler', StandardScaler(with_mean=False)), ('mutual_info', feat_sel), ('logistregress', clf)]) y_pred = model_selection.cross_val

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am using ROCR package and i was wondering how can one plot a ROC curve for knn model in R? Is there any way to plot it all with this package? I don't know how to use the prediction function of ROCR for knn. Here's my example, i am using isolet dataset from UCI repository where i renamed the class attribute as y: cl<-factor(isolet_training$y) knn_isolet<-knn(isolet_training, isolet_testing, cl, k=2, prob=TRUE) Now my question is, what are the arguments to pass to the prediction function of ROC. I tried the 2 below alternatives which are not

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am trying to find ROC curve and AUROC curve for decision tree. My code was something like clf.fit(x,y) y_score = clf.fit(x,y).decision_function(test[col]) pred = clf.predict_proba(test[col]) print(sklearn.metrics.roc_auc_score(actual,y_score)) fpr,tpr,thre = sklearn.metrics.roc_curve(actual,y_score) output: Error() 'DecisionTreeClassifier' object has no attribute 'decision_function' basically, the error is coming up while finding the y_score. Please explain what is y_score and how to solve this problem? 回答1: First of all, the

I've built a binary classifier using Tensorflow and now I would like to evaluate the classifier using AUC and accuracy. As far as accuracy is concerned, I can easily do like this: X = tf.placeholder('float', [None, n_input]) y = tf.placeholder('float', [None, n_classes]) pred = mlp(X, weights, biases, dropout_keep_prob) correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) When calculating AUC I use the following: print(tf.argmax(pred, 1).dtype.name) print(tf.argmax(pred, 1).dtype.name) a = tf.cast(tf.argmax(pred, 1)