A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a true positive and a true negative). They are also known in medicine as a false positive (or false negative) diagnosis, and in statistical classification as a false positive (or false negative) error. In statistical hypothesis testing the analogous concepts are known as type I and type II errors, where a positive result corresponds to rejecting the null hypothesis, and a negative result corresponds to not rejecting the null hypothesis. The terms are often used interchangeably, but there are differences in detail and interpretation due to the differences between medical testing and statistical hypothesis testing. (Wikipedia).
The Probability of a False Positive in a Drug Test
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From playlist Statistics
Ex: Simplifying the Opposites of Negatives Integers
This video provides several examples of simplifying opposites of negative integers. Search Complete Video Library at http://www.mathispower4u.wordpress.com
From playlist Introduction to Integers
Why Does a Negative Times a Negative Equal a Positive
This tutorial uses basic math and logic to demonstrate that a negative times a negative equals a positive. Join this channel to get access to perks: https://www.youtube.com/channel/UCn2SbZWi4yTkmPUj5wnbfoA/join :)
From playlist Basic Math
Proving a Negative Times a Negative Is a Positive with the Distributive Property
When you're multiplying integers and especially when you begin multiplying negative numbers, one of the first questions that comes up for students is why does a negative times a negative equal a positive? There are lots of ways to show it, and a couple of my favorites are: + Multiplicatio
From playlist Math Mini
Probability of a Negative Result Given the Disease
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From playlist Statistics
A look at why negative numbers multiply and divide to get positive products or quotients.
From playlist Core Standards - 7th Grade Math
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 12 - classifiers
Professor Sanjay Lall Electrical Engineering To follow along with the course schedule and syllabus, visit: http://ee104.stanford.edu To view all online courses and programs offered by Stanford, visit: https://online.stanford.edu/ 0:00 Introduction 0:11 Categorical outputs 12:07 Applic
From playlist Stanford EE104: Introduction to Machine Learning Full Course
Prealgebra 3.04d - Multiplying Fractions that are Negative
The rules pertaining to multiplying negative numbers also apply to negative fractions.
From playlist Prealgebra Chapter 3 (Complete chapter)
Intermediate Algebra Lecture 11.4: Solving Non-Linear and Quadratic Inequalities.
https://www.patreon.com/ProfessorLeonard Intermediate Algebra Lecture 11.4: Solving Non-Linear and Quadratic Inequalities.
From playlist Intermediate Algebra (Full Length Videos)
Lecture4. Customer relationship management. Churn prediction.Classification.
Data Science for Business. Lecture 4 slides: https://drive.google.com/file/d/1J_Ufp6MtMQp_L2JI3nh9xQYsu6LDEdm8/view?usp=sharing
From playlist Data Science for Business, 2022
ROC (Receiver Operating Characteristic) Curve in 10 minutes!
The ROC curve is a very effective way to make decisions on your machine learning model based on how important is it to not allow false positives or false negatives. In this video we introduce the ROC curve with a simple example. Grokking Machine Learning Book: https://www.manning.com/book
From playlist General Machine Learning
The ROC Curve (Receiver-Operating Characteristic Curve) — Topic 84 of Machine Learning Foundations
#MLFoundations #Calculus #MachineLearning In this video, we work through a simple example — with real numbers — to demonstrate how to calculate the Receiver-Operating Characteristic Curve (the ROC Curve), an enormously useful metric for quantifying the performance of a binary classificati
From playlist Calculus for Machine Learning
Stanford Seminar - Incorporating Sample Efficient Monitoring into Learned Autonomy
January 20, 2023 Rachel Luo of Stanford University When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial. Warning systems are thus designed to provide alerts when an unsafe situation is imminent (in the absence of c
From playlist Stanford AA289 - Robotics and Autonomous Systems Seminar
Stanford ENGR108: Introduction to Applied Linear Algebra | 2020 | Lecture 38 - VMLS classification
Professor Stephen Boyd Samsung Professor in the School of Engineering Director of the Information Systems Laboratory To follow along with the course schedule and syllabus, visit: https://web.stanford.edu/class/engr108/ To view all online courses and programs offered by Stanford, visit:
From playlist Stanford ENGR108: Introduction to Applied Linear Algebra —Vectors, Matrices, and Least Squares
solving and expression with absolute value and negative numbers
From playlist Common Core Standards - 7th Grade
Prob & Stats - Bayes Theorem (21 of 24) Effects of the Test Results: Example 2
Visit http://ilectureonline.com for more math and science lectures! In this video I will determine the sensitivity, the specificity, positive predictive value (PPV) and negative predictive values (NPV) of a test given: out of 100 subjects, 20 subjects will have the disease and 80 are heal
From playlist PROB & STATS 4 BAYES THEOREM