Questions tagged [auc]

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8 views

Model performance in different snapshots varying

I am trying to solve this problem. A medical representative needs to visit some doctors' clinics and for that a model will generate probability scores for visiting a clinic. I ma using a tree based ...
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8 views

LightGBM model improvement when the focus is on probability prediction

I am building a binary classifier using LightGBM. The goal is not to predict the outcome as such, but rather to predict the probability of the target even. To be more specific, it's more about ranking ...
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1answer
54 views

Overall AUC higher than all “stratified” AUCs

For one of my binary classification models, I have observed this (Simpson's Rule-esque) paradox. The AUC on the test set as a whole is 0.8. Gender is one of the model's features. So I decided to ...
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1answer
44 views

Implementing the Trapezoid rule without the formula for the curve

I know that if I have some function f(x) that describes a curve, I can approximate the area under the curve using the trapezoid rule as follows: ...
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2answers
90 views

Main options on how to deal with imbalanced data

As far as I can tell, broadly speaking, there are three ways of dealing with binary imbalanced datasets: Option 1: Create k-fold Cross-Validation samples randomly (or even better create k-fold ...
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1answer
58 views

At what stage are ROC curves used when building machine learning model?

When developing a machine learning model, at what stage are ROC curve with AUC used? Typically I have three data sets train - ...
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0answers
44 views

Best practice to select precision vs. recall threshold for a binary classifier

I have a logistic regression model in Scikit-Learn doing a binary classification. Looking at the Roc curve for the classifier I can see that it performs really well: The AUC score is 0.99 which is ...
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20 views

When to use AUROC OvR vs. AUROC OvO?

For multiclass problems, there are 2 versions of the AUROC metric: the AUROC OvR and AUROC OvO. Does anyone know in what particular cases we would use AUROC OvR vs. AUROC OvO? In the general academic ...
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2answers
57 views

Is the PR AUC invariant under label flip?

The ROC-AUC curve is invariant under a flip of the labels. I don't know if its a famous result so I will give the proof below. My question is if the PR-AUC curve also has this property. I have not ...
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2answers
45 views

Will oversampling help with generalization (small imbalanced dataset)?

I have an imbalanced dataset (2:1 ratio) with about 60 patients and 80 features. I performed Recursive Feature Elimination (RFE) and stratified cross validation to reduce the features to 15 and I get ...
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0answers
46 views

Orange ROC analysis widget [closed]

ROC analysis widget has an option to change prior target class probability. I'd like to know, when and how it should be used. Playing with it changes the slope of iso-performance line. Each classifier ...
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1answer
251 views

Micro Average vs Macro Average for Class Imbalance

I have a dataset consisting of around 30'000 data points and 3 classes. The classes are imbalanced (around 5'000 in class 1, 10'000 in class 2 and 15'000 in class 3). I'm building a convolutional ...
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0answers
25 views

Fast PR / ROC curves and corespondings AUPR / AUROC

I find myself in a position of calculating numerous PR / ROC curves and their associated area under the PR curves (AUPR) / area under the ROC curve (AUROC). Its is quite easy to perform those ...
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0answers
220 views

AUC on ROC Curve near 1.0 for Multi-Class CNN but Precision/Recall are not perfect?

I am building a ROC Curve and calculating AUC for multi-class classification on the CIFAR-10 dataset using a CNN. My overall Accuracy is ~ 90% and my precision and recall are as follows: ...
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1answer
153 views

AUC ROC Curve multi class Classification

Here is the part of the code for ROC AUC Curve calculation for multiple classes. ...
1
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1answer
45 views

Algorithm for Binary classification

I have a data set with huge number of features ( Approximately 3000) and a binary target variable . The reason I have too many features is because of one hot encoding many categorical variables in ...
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0answers
27 views

Trying to plot REC curve and calculate AUC in R but getting error

I am working on regression machine learning techniques and calculating AUC (plotting REC) by using below paper link: http://homepages.rpi.edu/~bennek/papers/rec.pdf My Code; REC <- function(y_val , ...
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2answers
121 views

Confused AUC ROC score

I am working on binary classification problem, I try to evaluate the performance of some classification algorithms (LR,Decission Tree , Random forest ...). I am using a cross validation technique (to ...
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0answers
16 views

How to compute a confidence interval for AUC?

I found that in results of several binary classification problems, people report an AUC value together with a CI. I wonder how those CIs are computed. Is there a close-formed formula to compute them ...
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2answers
276 views

Don't understand why I get an inverse ROC curve for SVM (Python)

I build an SVM classifier but get an inverse ROC curve. The AUC is only 0.08. I've used the same datasets to build a Logistic Regression classifier and a Decision Tree classifier, and the ROC curves ...
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0answers
18 views

confused AUC ROC score [duplicate]

I am working on binary classification problem, I try to evaluate the performance of some classification algorithms (LR,Decission Tree , Random forest ...). I am using a 10 fold cross-validation ...
0
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1answer
36 views

XG Boost result interpretation for unbalanced datasets (Accuracy & AUCROC)

My dataset is of shape – 5621*8 (binary classification) Label/target : Success (4324, 77 %) & Not success (1297, 23 %) (success and Not success were been ...
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2answers
2k views

AUC-ROC for Multi-Label Classification

Hey guys I'm currently reading about AUC-ROC and I have understood the binary case and I think that I understand the multi-classification case. Now I'm a bit confused on how to generalize it to the ...
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2answers
137 views

Getting lower performance metrics when using GridSearchCV

I have defined an XGBoost model and would like to tune some of its hyperparameters. I am using GridSearchCV to find the best params. However, I also tried to fit the model on the entire training ...
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2answers
253 views

How to compute AUC in gridsearchSV (multiclass problem)

I'm working on a multiclass classification problem, comparing results from SVM and Random Forest classificators. I would like to use gridsearchCV for hyperparameters tuning and find that AUC is the ...
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0answers
13 views

What is a right way to compare AUCROC between logreg and logregs on segments?

I built a logistic regression on the entire sample (first model), as well as regressions on separate pieces (disjoint) of same sample (set of models). What is the best way to compare the effectiveness ...
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1answer
162 views

Confusion matrix and ROC AUC curves are not in sync

I created a classification model with three target classes and created a confusion matrix to measure the accuracy, here is the matrix code ...
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2answers
2k views

Plotting ROC & AUC for SVM algorithm

Towards , the end of my program, I have the following code. model = svm.OneClassSVM(nu=nu, kernel='rbf', gamma=0.00001) model.fit(train_data) Output ...
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1answer
45 views

Confusion Matrix and AUC in univariate Anomaly Detection

In the code I upon a csv file which only has one column. The data in there in not that important just normal numbers. ...
2
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0answers
15 views

Connection between prob output LogisticReg/SVM and ROC

I have the following ROC generated using LPOCV and Logistic regression or SVM (l2 norm). Now, let's say I have a test set containing 10 patients and I get that the probabilities of those patients to ...
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1answer
172 views

Imbalanced dataset - Positive majority class

My dataset consists of 150 patients where 50 are controls/healthy (negative) and 100 are sick (positive). If I want my model to have high sensitivity at hight specificity, in other words to have low ...
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2answers
40 views

How much can the AUC improve comparing the raw dataset and the feature engineered dataset?

Let's say I put the following two datasets in the best possible model (same model for both): A raw dataset, the variables as they came just from the query. A feature-engineered dataset, with hundreds ...
2
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2answers
133 views

What is AUC - ROC Curve?

AUC - ROC curve is a performance measurement for classification problem at various thresholds settings. ROC is a probability curve and AUC represents degree or measure of separability. Is Roc the ...
1
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1answer
11 views

Truncating float/doubles for reproducibility

I deploy machine learning models (typically GPU) to a variety of environments. I work sort of at the edge of ML R&D and devops, so I am really big into reproducibility, and one thing that drives ...
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0answers
226 views

How to create an roc plot and calculate AUC for an svm (that does not return probabilities)?

I have some SVM classifier outputting final classifications for every sample in the test set, something like 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1 and so on. The "...
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1answer
491 views

XGBOOST missing_value feature degrades my performance?

I'm training an xgboost model for gout disease on a training set I sampled 1-to-7 case-control ratio (enriched in cases). I have 220 features and I reach a cross-...
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0answers
107 views

Regression for binary classification and AUC metric

In the kaggle forums I found an example model where someone was using XGBRegressor for a binary (0/1) classification problem (sorry, cannot find the link any more). This was for a competition where ...