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Questions tagged [false-positive-rate]

In a test for a condition (such as a disease) the false positive rate is the proportion of subjects incorrectly classified as having the condition.

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False Negative vs False Positive for Multiclass classification

Suppose I have three classes 1,2,3. And there's evaluation like below, where second element is false prediction where model predict class 3 while ground truth is 2. ...
Muhammad Ikhwan Perwira's user avatar
3 votes
1 answer
182 views

Is it possible to adjust a logistic regression model when the false positive rate is too high for observations belonging to a specific category?

This is a hypothetical scenario for self-learning purposes (not homework), so not a lot of additional details to give than what I mention below. Let's say I have a binary logistic regression model, ...
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FDR Correction needed on a pixel-wise comparison?

I have a question, which twirls my mind but I can't find a robust basis to answer it! I have a time-frequency data (let's say 5 columns x 5 rows) for each participant in two different groups. I have ...
KhonsKhandr's user avatar
2 votes
1 answer
26 views

Create dataset by sampling from near-boundary of binary classifier to improve accuracy

Say I have some binary classifier $f: X \to [0, 1]$. I think the following bi-stage training method is straightforward to reduce error. Step1. Sample uniformly from $X$ and create dataset with ...
orematasaburo's user avatar
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Calculate probability of alarm and the posterior probability of this alarm being false over different frequencies of output

I have the following information for an automatic detection system that output a warning when a signal is detected: Specificity: .99 (i.e. a false positive rate $FP = .01$) Sensitivity: .9 (i.e. a ...
fednem's user avatar
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1 answer
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If AUC is 1 can be FNR 0.03?

If AUC (Area under Curve) is 1, do we expect FNR (False Negative Rate) be 0? For example, can FNR be 0.03?
Peter's user avatar
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2 votes
1 answer
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How to interpret ROC curve if my FPR (X-axis) values ranges from 0 to 0.02?

I am plotting the ROC curve for my classification problem. The results I am getting for the problem are TPR ranges from 0 to 1, but the FPR ranges from 0 to 0.02. I have plotted the ROC curve by ...
Rajesh Ahir's user avatar
1 vote
0 answers
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Can a low rate of true positives minimise false negatives?

I am analysing thousands of scientific pictures which all show a similar light pattern, trying to identify local anomalies on the pictures. As it is nearly impossible to inspect each single image due ...
Jo Brick's user avatar
2 votes
0 answers
88 views

Does threshold on the model probability depend upon the spread in the dataset among positive and negative classes (binary classification)?

I think that the threshold on model probability through which one discern positive (y=0) and negative(y=1) class depends on the spread in the training dataset b/w y=0 and y=1. This question came when ...
Mins's user avatar
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3 votes
2 answers
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why I am getting TP and FP in confusion matrix is 0, how to get it right?

why I am getting TP and FP in confusion matrix is 0, how to get it right? tp = 0,fp = 0,tn = 9847,fn = 18
Abhishek Pandey's user avatar
5 votes
2 answers
194 views

When using ROC curves for WWII Radars, what was the TN?

One of the origins of ROC curves seems to be to compare radar systems in WWII (source). How did they actually compute the False Positive Rate when they didn't have an estimate for True Negatives? If I ...
brnl's user avatar
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1 answer
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False and true positives in research

Let's suppose that there's a new apple tree disease, and there exists a test for the disease that has a false positive rate of 5%, the test produces no false negatives (if an apple tree has the ...
Canada's user avatar
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6 votes
1 answer
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Relationship between Recall, TPR, FPR and Precision

Can Precision and Recall be used to Generate TPR or FPR? In other words, is there any formula that relates the following Evaluation metrics? True Positive Rate (TPR) with either Precision or Recall (...
Maryam's user avatar
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102 views

Analytic expression for false-negative rate of binomial tests?

I wrote a previous question yesterday which was maybe too long and boring to read. So to try to get an answer, I've boiled down my question to something short and specific which is: Is there an ...
Steven Sagona's user avatar
9 votes
3 answers
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How many coin flips are needed to reliably know a coin of weight w is unfair?

I want to find out how many flips I need to flip a coin to reliably know that it is an unfair coin. The issue is that as the coin becomes closer to 50/50, the more false-negatives you will have if you ...
Steven Sagona's user avatar
3 votes
1 answer
218 views

Is Partial Correlation useful for noisy data?

Explaining The Problem Important question in data analysis is testing observed relationships for confounding factors. Partial Correlation is a metric designed to do specifically that. The general idea ...
Aleksejs Fomins's user avatar
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Reduce false positive in extremely high imbalance testing set

I have built a CNN model to classify positive and negative in my data, the accuracy is around 85% with FPR is 16%. I know the FPR is high but it gives an acceptable number of FP in training and ...
Anna's user avatar
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3 votes
2 answers
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ROC curve from an array of Confusion Matrices (true positive rates and false positive rates)

How can we create an ROC curve from an array of Confusion Matrices (true positive rates and false positive rates)?
Vic's user avatar
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11 votes
2 answers
20k views

Best way to reduce false positive of binary classification to exactly 0?

I'm working on a task that even a 0.00001 fp rate is not acceptable, because detecting something as a positive when its not will have very bad consequences in this task, so it needs to be exactly 0 ...
OneAndOnly's user avatar
3 votes
1 answer
645 views

Formula for expected false positive & negative rates in hiring decisions based on r

I would like to look at the size of the expected false positive and false negative rates in employment hiring decisions. Let's assume that it is useful to dichotomize job performance after hiring. ...
Joel W.'s user avatar
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5 votes
2 answers
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Does up-sampling lead to lots of false positives in production?

Say we have a dataset with a binary outcome variable that takes the positive case (outcome = 1) roughly 20% of the time. Often, we would modify the training set by ...
AmeySMahajan's user avatar
1 vote
0 answers
50 views

Sensitivity and specificity with multiple sequential tests

This has been on my mind for a while, and I would appreciate a statistician's view on the matter. Suppose that a given test has sensitivity $a$ and specificity $b$. Now, periodically apply this test ...
Santiago Nuñez-Corrales's user avatar
4 votes
1 answer
305 views

Estimating positive and negative predictive value without knowing the prevalence

There is a lot of discussion about the positive predictive value of a test currently. I know that if I know specificity, sensitivity of a test and the prevalence $p$ in the sample, then I can easily ...
LiKao's user avatar
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Correlation between two non-independent samples (dichotomized data / multiple testing)

I've been looking through text books but unable to find a precise answer to this question, but it seems important so maybe I'm looking in wrong places. Imagine some population with a normal ...
DRG's user avatar
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104 views

How do you cope with the risk of false-positives in exploratory analysis?

Let's say that I'm running exploratory analysis on a dataset. For instance, let's say that the dataset consists of several features and two groups and I want to see which features are significantly ...
user avatar
4 votes
2 answers
81 views

Aren't all tests sensitive to the prevalence of a disease in the population?

I'm trying to understand the difference between the false-positive rates of two kinds of COVID-19 tests: PCR and antibody. The former indicates if someone is currently sick. The latter indicates if ...
Gili's user avatar
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0 answers
979 views

How can I fix type 1 error for my logistic regression model?

I am testing if the sound pressure levels(rms) of shipping noise affect the presence or absence of the fin whales by acoustic monitoring for 4 months of data from one location. Here my response ...
Kavya Ramesh's user avatar
1 vote
1 answer
183 views

How to bias against false positives given estimate and standard deviation?

For 1000 newsletter recipients, I crudely estimate the likelihood (p) of them reading the next email sent as: ...
stevec's user avatar
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0 votes
1 answer
209 views

If a false positive rate of α is desired, what is the acceptable range of false positives one would expect from a sample size of n?

For instance, suppose I design a test to have a false positive rate of 5%. I am going to perform this test on what is assumed to be an all negative population. If my sample size was 20, I would expect ...
S. Melted's user avatar
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1 vote
0 answers
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Procedure to Smooth Noise for Threshold Values

Suppose I am looking at an estimate $\hat\beta$ in a clinical trial data. With $a=.05$, a patient with $\hat\beta>1$ is considered to have some medical condition. A daily example could be a 24-hr ...
Frank Swanton's user avatar
2 votes
1 answer
996 views

Base rate of accuracy after resampling for classification problems

If I had an imbalanced dataset with 10% positive instances and 90% negative ones, the base rate for accuracy before resampling is 90%. But what about I resampled the data such that I have an equal ...
Chong Sun's user avatar
1 vote
0 answers
134 views

Deriving a loss function properly accounting for different error type costs

Consider a classification setting like a medical test: Not finding an existing health issue might be much worse (by a factor of 50) than assuming an issue when there is none. I.e. a setting in which ...
Hauptideal's user avatar
2 votes
1 answer
148 views

What is "positive"?

I'm deeply confused by some concepts. We often hear the term true/false positive/negative. While it is straightforward to tell if the result is true or false, I ...
youkaichao's user avatar
1 vote
2 answers
409 views

FPR in Confusion Matrix

I was trying to manually calculate $\text{TPR}$ and $\text{FPR}$ for the given data. But unfortunately I dont have any false positive cases in my dataset and even no true positive cases. So I am ...
Maagalam HARSHA VARDHAN's user avatar
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0 answers
275 views

Oversampling/Undersampling in respect to Train and Test - Isolation Forest

I've got a quite imbalanced data set. 144.496 : 162 -> ratio of 1000:1 I would like to use IsolationForest to detect the 162 anomalys. I've already split the data. However, the iForest doesn't ...
Marvin's user avatar
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2 votes
1 answer
715 views

False positive/negative rate in ridge and lasso regressions

I have a confusion matrix of true and estimated $\boldsymbol{\beta}$ vectors of lasso and ridge models from a replicate of a simulation study, say. The following tables illustrate the scenario. $$\...
mert's user avatar
  • 313
0 votes
2 answers
254 views

How to make sense of both high FNR and NPV?

So, even though this is one of the most basic and already explained things about statistics I always seem to find way not to get it. Having this table which shows high Negative Predictive Value (352/...
N.S.I's user avatar
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1 vote
1 answer
165 views

SVM probability output threshold as (1 - FPR)?

I have a binary SVM with probability output (via Platt scaling). I want to set a threshold on the probability outputs since I want to trade off making false positives/negatives. Is it possible to ...
Philipp's user avatar
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2 votes
0 answers
103 views

How to interpret a False Discovery Rate plot

It is the first time that I am trying to calculate the FDR and I use the fdrtool package in R. I want both, local and tail area graphs and I think the third ...
foo's user avatar
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0 answers
13 views

Estimating false accept rates from imposter scores below a threshold

I have a system that compares two items and produces a match score. Scores below a threshold are manually inspected to determine if they match or don't(imposter). Scores above the threshold are ...
Cavalent's user avatar
2 votes
1 answer
233 views

Is most published research indeed false?

I have come across (Ioannidis, 2005) which explains several reasons (mainly statistics-related, that's why I post this question here) to justify the claim that most published research is indeed false. ...
David's user avatar
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0 votes
1 answer
42 views

Optimal solution for false positive effect on business strategy

I am facing a challenge to reach an optimal solution. I will try to explain with an example : Suppose I have created an algorithm to predict if a customer will subscribe to bank deposit plan or not. ...
Aniket's user avatar
  • 1
0 votes
1 answer
226 views

Is it possible to estimate accuracy, precision and recall with the given data?

Background: I talked to my friend today and according to herm(him/her) I can calculate precision, recall and accuracy with the current information. Total instances T: 19,532. Instances belonging to ...
sockevalley's user avatar
1 vote
0 answers
43 views

Exclude areas of feature space without getting false negatives

I am using a decision tree classifier to split the feature space according to two classes ( A and B). Events of class A are important and I want to classify all of them correct, i.e. no false ...
Pete's user avatar
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0 votes
0 answers
29 views

Estimating true positive rate with a confidence interval given a classifier + unknown dataset truths

Let's say I've built a binary classifier - one that for instance, can classify whether a particular transaction is fraudulent or not. This classifier outputs a 1 or a 0, with a given degree of ...
jeet.m's user avatar
  • 101
2 votes
2 answers
734 views

Is it the case that β+power=1?

In psychology papers that do prospective power analyses (example), one often notices the convention of assuming β values (false negative rates) of 0.20, and power levels of 0.80. In other words (if I ...
z8080's user avatar
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2 votes
0 answers
281 views

What is the integral of the False Positive Rate over the False Positive Rate, compared to the AUC?

In machine learning the Area Under the Receiver Operating Characteristic Curve ($AUC$) can be illustrated in a plot of the True Positive Rate ($TPR$) against the False Positive Rate ($FPR$). Formally, ...
jb123's user avatar
  • 31
3 votes
1 answer
94 views

How to make really bad results from a machine learning model better by reversing predictions

I trained a classification model on some data with two classes and have really low accuracy. I have a false-positive rate of 86 % for both classes I am trying to predict. I was wondering if I could ...
Samuel Polk's user avatar
3 votes
2 answers
353 views

Number of samples required to estimate a desired False Positive Rate

I have an algorithm that for each sample $x_i$ returns an anomaly score $0<s_i<1$. I use cross validation to set a threshold $th$ such that $x_i$ is anomalous if $s_i>th$. During cross ...
Donbeo's user avatar
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2 votes
1 answer
235 views

How to tell the likelihood of getting a false positive?

Say we have a test for a disease we are comparing with the gold standard. You are given the prevalence of the disease, which is 1%, and sensitivity, 80%, and specificity, 90% and a total population of ...
Andres Felipe Borrero Gonzalez's user avatar