You can use the add = TRUE argument the plot function to plot multiple ROC curves. Make up some fake data library(pROC) a=rbinom(100, 1, 0.25) b=runif(100) c=rnorm(100) Get model fits fit1=glm(a~b+c, family='binomial') fit2=glm(a~c, family='binomial') Predict on the same data you trained the model with (or hold some out to test...

As the error message say, you need a numeric vector or ordered factor in lr.pred. The problem here is that predict (for the svm) returns the predicted class, making the ROC exercise pretty much useless. What you need is to get an internal score, like the class probabilities: lr.pred <-...

A sample example for AUC: rf_output=randomForest(x=predictor_data, y=target, importance = TRUE, ntree = 10001, proximity=TRUE, sampsize=sampsizes) library(ROCR) predictions=as.vector(rf_output$votes[,2]) pred=prediction(predictions,target) perf_AUC=performance(pred,"auc") #Calculate the AUC value [email protected][[1]] perf_ROC=performance(pred,"tpr","fpr") #plot the actual ROC curve plot(perf_ROC, main="ROC plot") text(0.5,0.5,paste("AUC = ",format(AUC, digits=5, scientific=FALSE))) or using pROC and caret library(caret)...

I assume you are using the pROC package. The x argument can be set to "all" as in coord_list[[1]] <- coords(roc_train, x = "all") This will return all coordinates of the ROC curve....

python,machine-learning,scikit-learn,roc

import pandas as pd import numpy as np import pylab as pl from sklearn.metrics import roc_curve, auc df = pd.read_csv('filename.csv') y_test = np.array(df)[:,0] probas = np.array(df)[:,1] # Compute ROC curve and area the curve fpr, tpr, thresholds = roc_curve(y_test, probas) roc_auc = auc(fpr, tpr) print("Area under the ROC curve :...

python,matplotlib,plot,seaborn,roc

Since seaborn also uses matplotlib to do its plotting you can easily combine the two. If you only what to adopt the styling of seaborn the set_style function should get you started: import matplotlib.pyplot as plt import numpy as np import seaborn as sns sns.set_style("darkgrid") plt.plot(np.cumsum(np.random.randn(1000,1))) plt.show() Result: ...

As per documentation the optimal cut-off point is defined as the point where Sensitivity + Specificity is maximal (see MX argument in ?ROC). You can get the according values as follows (see example in ?ROC): x <- rnorm( 100 ) z <- rnorm( 100 ) w <- rnorm( 100 )...

python,validation,machine-learning,scikit-learn,roc

The number of points depend on the number of unique values in the input. Since the input vector has only 2 unique values, the function gives correct output.

This will work: roc(myffdf$outcome[], pred) Note the square brackets. Thanks to user20650 and JVL...

You cannot generate the full ROC curve with a single contingency table because a contingency table provides only a single sensitivity/specificity pair (for whatever predictive cutoff was used to generate the contingency table). If you had many contingency tables that were generated with different cutoffs, you would be able to...