roc | 易学教程

How does sklearn actually calculate AUROC?

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问题 I understand that the ROC curve for a model is constructed by varying the threshold (that affects TPR, FPR). Thus my initial understanding is that, to calculate the AUROC, you need to run the model many times with different threshold to get that curve and finally calculate the area. But it seems like you just need some probability estimate of the positive class, as in the code example in sklearn's roc_auc_score below, to calculate AUROC. >>> import numpy as np >>> from sklearn.metrics import

How to plot a ROC curve using ROCR package in r, with only a classification contingency table

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问题 How to plot a ROC curve using ROCR package in r, with only a classification contingency table ? I have a contingency table where the true positive, false positive.. etc. all the rated can be computed. I have 500 replications, therefore 500 tables. But, I can not generate a prediction data indicating each single case of estimating probability and the truth. How can I get a curve without the individual data. Below is the package instruction used. ## computing a simple ROC curve (x-axis: fpr, y

学习笔记之scikit-learn

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scikit-learn: machine learning in Python — scikit-learn 0.20.0 documentation https://scikit-learn.org/stable/index.html Simple and efficient tools for data mining and data analysis Accessible to everybody, and reusable in various contexts Built on NumPy, SciPy, and matplotlib Open source, commercially usable - BSD license scikit-learn - Wikipedia https://en.wikipedia.org/wiki/Scikit-learn Scikit-learn (formerly scikits.learn ) is a free software machine learning library for the Python programming language. [3] It features various classification , regression and clustering algorithms including

Plotting a ROC curve in scikit yields only 3 points

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问题 TLDR: scikit's roc_curve function is only returning 3 points for a certain dataset. Why could this be, and how do we control how many points to get back? I'm trying to draw a ROC curve, but consistently get a "ROC triangle". lr = LogisticRegression(multi_class = 'multinomial', solver = 'newton-cg') y = data['target'].values X = data[['feature']].values model = lr.fit(X,y) # get probabilities for clf probas_ = model.predict_log_proba(X) Just to make sure the lengths are ok: print len(y) print

How to directly plot ROC of h2o model object in R

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问题 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

How to interpret almost perfect accuracy and AUC-ROC but zero f1-score, precision and recall

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问题 I am training ML logistic classifier to classify two classes using python scikit-learn. They are in an extremely imbalanced data (about 14300:1). I'm getting almost 100% accuracy and ROC-AUC, but 0% in precision, recall, and f1 score. I understand that accuracy is usually not useful in very imbalanced data, but why is the ROC-AUC measure is close to perfect as well? from sklearn.metrics import roc_curve, auc # Get ROC y_score = classifierUsed2.decision_function(X_test) false_positive_rate,

ROC curve from training data in caret

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问题 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

Sum of previous rows in a column R

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问题 I have table as following id State 1 True 2 False 3 True 4 False 5 False 6 True 7 True 8 False I need to count true and false until showed row . So the result should be as the following table id State Yes No 1 True 1 0 2 False 1 1 3 True 2 1 4 False 2 2 5 False 2 3 6 True 3 3 7 True 4 3 8 False 4 4 Until 6th(including 6th) row there are 3 False and 3 True. Any ideas? 回答1: Does this do what you want? df$yes <- cumsum(df$State == "True") df$no <- cumsum(df$State == "False") Or if you have df

How to use prediction score in creating ROC curve with Scikit-Learn

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问题 I have the following code: from sklearn.metrics import roc_curve, auc actual = [1,1,1,0,0,1] prediction_scores = [0.9,0.9,0.9,0.1,0.1,0.1] false_positive_rate, true_positive_rate, thresholds = roc_curve(actual, prediction_scores, pos_label=1) roc_auc = auc(false_positive_rate, true_positive_rate) roc_auc # 0.875 In this example the interpretation of prediction_scores is straightforward namely, the higher the score the more confident the prediction is. Now I have another set of prediction

Draw ROC curve in python using confusion matrix only

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问题 I need to draw ROC curve using confusion matrix only. Actually my system was crashed ( every information was lost), therefore I am not getting data. I have only values of confusion matrix. I know how to create ROC curve (http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc_crossval.html), but not getting any clue how to draw from confusion matrix. Please help me in this regards. 回答1: Unfortunately you can't build a ROC curve from a single contingency matrix. A ROC curve shows