Statistical classification

False positives and false negatives

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).

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The Probability of a False Positive in a Drug Test

Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys The Probability of a False Positive in a Drug Test

From playlist Statistics

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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

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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

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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

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Probability of a Negative Result Given the Disease

Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Probability of a Negative Result Given the Disease

From playlist Statistics

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Negative Numbers - Core N2a

A look at why negative numbers multiply and divide to get positive products or quotients.

From playlist Core Standards - 7th Grade Math

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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

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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)

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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)

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Lecture4. Customer relationship management. Churn prediction.Classification.

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From playlist Data Science for Business, 2022

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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

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The ROC Curve (Receiver-Operating Characteristic Curve) — Topic 84 of Machine Learning Foundations

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From playlist Calculus for Machine Learning

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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

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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

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absolute value and negatives

solving and expression with absolute value and negative numbers

From playlist Common Core Standards - 7th Grade

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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

Related pages

Type I and type II errors | Positive and negative predictive values | False positive rate | Prior probability | P-value | Statistical classification | Receiver operating characteristic | Why Most Published Research Findings Are False | Sensitivity and specificity | Binary classification | Statistical hypothesis testing | Null hypothesis