Evaluation of binary classifiers

IAML8.12 Evaluating classification and regression

From playlist Generalization and Evaluation

IAML2.21: Binary vs. multiclass classifiers

From playlist Thinking about Data

IAML5.1: Overview

From playlist Naive Bayes Classifier

Bayes Classifiers (1)

Bayes Classifiers; Bayes rule; discrete and Gaussian class-conditional distributions

From playlist cs273a

Hinge Loss for Binary Classifiers

From playlist Iterative Algorithms for Least Squares and Classification Problems

An Introduction to Classification

Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Develop predictive models for classifying data. For more videos, visit http://www.mathworks.com/products/statistics/examples.html

From playlist Math, Statistics, and Optimization

Performance Metrics and Evaluation

From playlist Data Science Course

CSE 519 -- Lecture 17, Fall 2020

From playlist CSE 519 -- Fall 2020

Confusion Matrix in Machine Learning | Binary and Multiclass Classification Examples | Edureka

🔥Edureka Data Scientist Course Master Program https://www.edureka.co/masters-program/data-scientist-certification (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") This Edureka tutorial explains the Confusion Matrix. How to construct confusion matrix for binary as well as multi class classification problems, vario

From playlist Data Science Training Videos

PNWS 2014 - Adding Tree and Tree: Distributed Decision Tree Learning

Avi Bryant Brushfire is a framework for distributed, supervised learning of ensembles of decision trees. It is designed to be extremely scalable (both in number of observations and number of features) and extremely customizable: it makes heavy use of Scala generics and typeclasses to allo

From playlist PNWS 2014

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

Applied Machine Learning 2019 - Lecture 10 - Model Evaluation

Metrics for binary classification, multiclass and regression. ROC curves, precision-recall curves. Class website with slides and more materials: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/

From playlist Applied Machine Learning - Spring 2019

Ensemble Learning | Ensemble Learning In Machine Learning | Machine Learning Tutorial | Simplilearn

🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=EnsembleLearning&utm_medium=Descriptionff&utm_source=youtube 🔥Professional Certificate Program In AI And Machine Learning: https://www.simplilear

From playlist 🔥Artificial Intelligence | Artificial Intelligence Course | Updated Artificial Intelligence And Machine Learning Playlist 2023 | Simplilearn

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

Logistic Regression

Overview of logistic regression, a statistical classification technique.

From playlist Machine Learning

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

Deep Learning for Natural Language Processing with Jon Krohn

Jon Krohn introduces how to preprocess natural language data. He then uses hands-on code demos to build deep learning networks that make predictions using those data. This lesson is an excerpt from "Deep Learning for Natural Language Processing LiveLessons, 2nd Edition." Purchase entire

From playlist Talks and Tutorials

Case Studies with Data: Mitigating Gender Bias on the UCI Adult Dataset

MIT RES.EC-001 Exploring Fairness in Machine Learning, Spring 2020 Instructor: Audace Nakeshimana View the complete course: https://ocw.mit.edu/RES-EC-001S20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63IFQn8FklBOUhYVcmaxpOX This video explores principles involved

From playlist MIT RES.EC-001 Exploring Fairness in Machine Learning, Spring 2020

Introduction to Classification Models

Ever wonder what classification models do? In this quick introduction, we talk about what classifications models are, as well as what they are used for in machine learning. In machine learning there are many different types of models, all with different types of outcomes. When it comes t

From playlist Introduction to Machine Learning