Classification algorithms | Statistical classification
In machine learning, one-class classification (OCC), also known as unary classification or class-modelling, tries to identify objects of a specific class amongst all objects, by primarily learning from a training set containing only the objects of that class, although there exist variants of one-class classifiers where counter-examples are used to further refine the classification boundary. This is different from and more difficult than the traditional classification problem, which tries to distinguish between two or more classes with the training set containing objects from all the classes. Examples include the monitoring of helicopter gearboxes, motor failure prediction, or the operational status of a nuclear plant as 'normal': In this scenario, there are few, if any, examples of catastrophic system states; only the statistics of normal operation are known. While many of the above approaches focus on the case of removing a small number of outliers or anomalies, one can also learn the other extreme, where the single class covers a small coherent subset of the data, using an information bottleneck approach. (Wikipedia).
Biological Classification of Hierarchy || #Shorts || Deveeka Ma'am || Infinity Learn Class 9&10
Biological classification is the scientific method of organizing and categorizing living organisms based on shared characteristics. This system allows us to study the diversity of life on Earth and understand how different species are related to one another. The hierarchy of biological cla
From playlist Shorts
Category Theory: The Beginner’s Introduction (Lesson 1 Video 4)
Lesson 1 is concerned with defining the category of Abstract Sets and Arbitrary Mappings. We also define our first Limit and Co-Limit: The Terminal Object, and the Initial Object. Other topics discussed include Duality and the Opposite (or Mirror) Category. These videos will be discussed
From playlist Category Theory: The Beginner’s Introduction
Category Theory: The Beginner’s Introduction (Lesson 1 Video 2)
Lesson 1 is concerned with defining the category of Abstract Sets and Arbitrary Mappings. We also define our first Limit and Co-Limit: The Terminal Object, and the Initial Object. Other topics discussed include Duality and the Opposite (or Mirror) Category. Follow me on Twitter: @mjmcodr
From playlist Category Theory: The Beginner’s Introduction
Category Theory: The Beginner’s Introduction (Lesson 1 Video 5)
Lesson 1 is concerned with defining the category of Abstract Sets and Arbitrary Mappings. We also define our first Limit and Co-Limit: The Terminal Object, and the Initial Object. Other topics discussed include Duality and the Opposite (or Mirror) Category. These videos will be discussed
From playlist Category Theory: The Beginner’s Introduction
One Versus One vs. One Versus All in Classification
In this quick machine learning tutorial, we introduce you to the concepts of one-versus-one and one-versus-all in classification. In classification models, you will often want to predict one class from another. This is called binary classification, or one-versus-one. But what if you have m
From playlist Data Science in Minutes
(0.3.101) Exercise 0.3.101: Classifying Differential Equations
This video explains how to classify differential equations based upon their properties https://mathispower4u.com
From playlist Differential Equations: Complete Set of Course Videos
From playlist Big Data Analytics by Dr. Emmanuel Müller
CSE 519 -- Lecture 21, Fall 2020
From playlist CSE 519 -- Fall 2020
From playlist Machine Learning Course
How to evaluate a classifier in scikit-learn
In this video, you'll learn how to properly evaluate a classification model using a variety of common tools and metrics, as well as how to adjust the performance of a classifier to best match your business objectives. I'll start by demonstrating the weaknesses of classification accuracy as
From playlist Machine learning in Python with scikit-learn
SETFIT Few-Shot Learning outperforms GPT-3 | SBERT Text Classification (SBERT 43)
NEW: SBERT Few-Shot Learning (SetFit) outperforms GPT-3 in text classification tasks. Few-shot learning without prompts: SetFit. Given a limited training sample set per class the new SetFit methodology based on SBERT Sentence Transformers perform exceptionally well in text classification.
From playlist SBERT: Python Code Sentence Transformers: a Bi-Encoder /Transformer model #sbert
Multiclass Classification : Data Science Concepts
How do we predict MORE than 2 classes??? My Patreon : https://www.patreon.com/user?u=49277905
From playlist Data Science Concepts
GT23. Composition and Classification
Abstract Algebra: We use composition series as another technique for studying finite groups, which leads to the notion of solvable groups and puts the focus on simple groups. From there, we survey the classification of finite simple groups and the Monster group.
From playlist Abstract Algebra
Introduction to Classification | Predictive Modeling and Machine Learning, Part 2
This video covers the basics of the most common machine learning classification models that you can tune to work with any number of predictor variables. Each has its advantages and disadvantages in terms of accuracy and training speed. The only way to know which one works best on a particu
From playlist Predictive Modeling and Machine Learning