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 was using k-fold cross validation in glmnet (which implements lasso regression), but I can’t make the ROC charts from this. library(glmnet) glm_net <- cv.glmnet(dev_x_matrix,dev_y_vector,family="binomial",type.measure="class") phat <- predict(glm_net,newx=val_x_matrix,s="lambda.min") That gets me a vector with what looks like a log of the fitted values. I was trying to generate some ROC charts after this but it did not work. I think it is because of the nature of the x and y objects which goes into the glmnet . Do you have any ideas. 回答1:

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): 由 翻译 强力驱动 问题: I have some machine learning results that I don't quite understand. I am using python sciki-learn, with 2+ million data of about 14 features. The classification of 'ab' looks pretty bad on the precision-recall curve, but the ROC for Ab looks just as good as most other groups' classification. What can explain that? 回答1: Class imbalance. Unlike the ROC curve, PR curves are very sensitive to imbalance. If you optimize your classifier for good AUC on an unbalanced data you are likely to obtain poor precision-recall results. 转载请标明出处:

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: Given such data: SN = Sensitivity; SP = Specificity Cutpoint SN 1-SP 1 0.5 0.1 2 0.7 0.2 3 0.9 0.6 How can i plot the ROC curve and calculate AUC. And compare the AUC between two different ROC curves. In the most of the packages such pROC or ROCR, the input of the data is different from those shown above. Can anybody suggest the way to solve this problem in R or by something else? ROCsdat <- data.frame(cutpoint = c(5, 7, 9), TPR = c(0.56, 0.78, 0.91), FPR = c(0.01, 0.19, 0.58)) ## plot version 1 op <- par(xaxs = "i", yaxs = "i") plot(TPR ~

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 trying to predict on the validation data with pre-trained and fine-tuned DL models. The code follows the example available in the Keras blog on "building image classification models using very little data". Here is the code: import numpy as np from keras.preprocessing.image import ImageDataGenerator import matplotlib.pyplot as plt from keras.models import Sequential from keras.models import Model from keras.layers import Flatten, Dense from sklearn.metrics import classification_report,confusion_matrix from sklearn.metrics import roc_auc

Create a classification dataset (n samples ! 1000, n features ! 10) Split the dataset using 10-fold cross validation Train the algorithms GaussianNB SVC (possible C values [1e-02, 1e-01, 1e00, 1e01, 1e02], RBF kernel) RandomForestClassifier (possible n estimators values [10, 100, 1000]) Evaluate the cross-validated performance Accuracy F1-score AUC ROC Write a short report summarizing the methodology and the results 只要按照ppt上的教程写代码即可，通过datasets.make_classification生成数据集，通过cross_validation.KFold将数据集划分为训练集和测试集，通过metrics.accuracy_score、metrics.f1_score、metrics.roc_auc_score获得结果。 from sklearn import

I am plotting ROCs and measuring partial AUC as a metric of ecological niche model quality. As I am working in R, I am using the ROCR and the pROC packages. I'll settle on one to use, but for now, I just wanted to see how they performed, and if one met my needs better. One thing that confuses me is that, when plotting a ROC, the axes are as follows: ROCR x axis: 'true positive rate' 0 -> 1 y axis: 'false positive rate', 0 -> 1 pROC x axis: 'sensitivity' 0 -> 1 y axis: 'specificity' 1 -> 0. But if I plot the ROC using both methods, they look identical. So I just want to confirm that: true

I'm trying to plot a ROC curve using seaborn (python). With matplotlib I simply use the function plot : plt.plot(one_minus_specificity, sensitivity, 'bs--') where one_minus_specificity and sensitivity are two lists of paired values. Is there a simple counterparts of the plot function in seaborn? I had a look at the gallery but I didn't find any straightforward method. hitzg Since seaborn also uses matplotlib to do its plotting you can easily combine the two. If you only want to adopt the styling of seaborn the set_style function should get you started: import matplotlib.pyplot as plt import

I am able to get a ROC curve using scikit-learn with fpr , tpr , thresholds = metrics.roc_curve(y_true,y_pred, pos_label=1) , where y_true is a list of values based on my gold standard (i.e., 0 for negative and 1 for positive cases) and y_pred is a corresponding list of scores (e.g., 0.053497243 , 0.008521122 , 0.022781548 , 0.101885263 , 0.012913795 , 0.0 , 0.042881547 [...]) I am trying to figure out how to add confidence intervals to that curve, but didn't find any easy way to do that with sklearn. ogrisel You can bootstrap the roc computations (sample with replacement new versions of y

Is it possible to plot the roc curve for diffrent classifiers in the same plot using the ROCR package? I've tried: >plot(perf.neuralNet, colorize=TRUE) >lines(perf.randomForest) But I get: Error en as.double(y) : cannot coerce type 'S4' to vector of type 'double' Thank you! The problem with your lines -approach is that there is no generic S4 lines function for an object of class performance defined in the ROCR package. But you can use the generic plot function as you did with an additional add = TRUE argument. For example this is partly from the example page of ?plot.performance : library(ROCR

My apologies if I'm missing something obvious. I've been thoroughly enjoying working with h2o in the last few days using R interface. I would like to evaluate my model, say a random forest, by plotting an ROC. The documentation seems to suggest that there is a straightforward way to do that: Interpreting a DRF Model By default, the following output displays: Model parameters (hidden) A graph of the scoring history (number of trees vs. training MSE) A graph of the ROC curve (TPR vs. FPR) A graph of the variable importances ... I've also seen that in python you can apply roc function here . But