In computer science, a logistic model tree (LMT) is a classification model with an associated supervised training algorithm that combines logistic regression (LR) and decision tree learning. Logistic model trees are based on the earlier idea of a model tree: a decision tree that has linear regression models at its leaves to provide a piecewise linear regression model (where ordinary decision trees with constants at their leaves would produce a piecewise constant model). In the logistic variant, the LogitBoost algorithm is used to produce an LR model at every node in the tree; the node is then split using the C4.5 criterion. Each LogitBoost invocation is warm-started from its results in the parent node. Finally, the tree is pruned. The basic LMT induction algorithm uses cross-validation to find a number of LogitBoost iterations that does not overfit the training data. A faster version has been proposed that uses the Akaike information criterion to control LogitBoost stopping. (Wikipedia).
Logistic Regression on the TI84
http://mathispower4u.wordpress.com/
From playlist TI-84: Regression on the Graphing Calculator
Logistic Growth Function and Differential Equations
This calculus video tutorial explains the concept behind the logistic growth model function which describes the limits of population growth. This shows you how to derive the general solution or logistic growth formula starting from a differential equation which describes the population gr
From playlist New Precalculus Video Playlist
This video is part of an online professional development course offered by the UNSW School of Mathematics and Statistics. https://www.openlearning.com/courses/populationgrowthandthelogisticcurve
From playlist Mathematics in The Modern World: PD courses for teachers
Logistic Regression - VISUALIZED!
People talk about "sigmoid functions", "decision boundaries" and “Training”. But what exactly is happening behind the scenes? Let’s see for ourselves! Please SUBSCRIBE to me for more content! Shoutout to 3blue1brown for creating his animation math engine “manim”. Give this a * on your
From playlist Logistic Regression
Code With Me : Logistic Regression (from scratch) !
Ever wonder how to code a logistic regression from first principles? Logistic Regression Video : https://www.youtube.com/watch?v=9zw76PT3tzs Maximum Likelihood Video : https://youtu.be/VOIhswqFWVc Link to Code : https://github.com/ritvikmath/YouTubeVideoCode/blob/main/Logistic%20Regress
From playlist Data Science Code
Lecture 0302 Hypothesis Representation
Machine Learning by Andrew Ng [Coursera] 03-01 Logistic Regression
From playlist Machine Learning by Professor Andrew Ng
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
Logistic Regression in R | Logistic Regression in R Example | Data Science Algorithms | Simplilearn
🔥Free Machine Learning Course: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearning&utm_medium=DescriptionFirstFold&utm_source=youtube This Logistic Regression in R video will help you understand what is a regression, the need for regression and th
From playlist Data Science For Beginners | Data Science Tutorial🔥[2022 Updated]
4.2.7 An Introduction to Trees - Video 4: CART in R
MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair Building a CART tree in R to predict the decisions of Justice Stevens and evaluate our model using a ROC curve. License: Creative Commons BY-NC-SA More informatio
From playlist MIT 15.071 The Analytics Edge, Spring 2017
Machine Learning Algorithms In-Depth Guide For 2022 | ML Algorithms Explained | Simplilearn
This video on Machine Learning Algorithm will take you through a detailed concept of machine learning algorithm. This video will help you to understand What is an Algorithm, What is Machine Learning, Types of Machine Learning, How Algorithms works in Machine Learning/Programing, Some popul
🔥Data Science Full Course 2023 | Data Science | Data Science For Beginners | Simplilearn
🔥 Enroll For Simplilearn's Data Science Job Guarantee Program: https://www.simplilearn.com/data-science-course-placement-guarantee?utm_campaign=DataScienceFC27nov2022&utm_medium=DescriptionFirstFold&utm_source=youtube This Data Science Full Course Video will provide you with a learning pat
From playlist Simplilearn Live
Applied ML 2020 - 08 - Gradient Boosting
Materials at https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/
From playlist Applied Machine Learning 2020
Applied ML 2020 - 10 - Calibration, Imbalanced data
Class materials at https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/
From playlist Applied Machine Learning 2020
Regression Trees, Clearly Explained!!!
Regression Trees are one of the fundamental machine learning techniques that more complicated methods, like Gradient Boost, are based on. They are useful for times when there isn't an obviously linear relationship between what you want to predict, and the things you are using to make the p
From playlist StatQuest
Classification In Machine Learning | Machine Learning Tutorial | Python Training | Simplilearn
🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=ClassificationInMachineLearning-xG-E--Ak5jg&utm_medium=Descriptionff&utm_source=youtube 🔥Professional Certificate Program In AI And Machine Learn
Logistic Regression Details Pt1: Coefficients
When you do logistic regression you have to make sense of the coefficients. These are based on the log(odds) and log(odds ratio), but, to be honest, the easiest way to make sense of these are through examples. In this StatQuest, I walk you though two Logistic Regression Examples, step-by-s
From playlist StatQuest
Data Science Algorithms Full Course | Data Science Tutorial For Beginners | Simplilearn
This video on Data Science algorithms will help you learn some of the essential data science algorithms used by data scientists to work on complex tasks. You will implement these algorithms using Python and R. These algorithms use supervised and unsupervised learning methods to train model
From playlist Simplilearn Live