A decision stump is a machine learning model consisting of a one-level decision tree. That is, it is a decision tree with one internal node (the root) which is immediately connected to the terminal nodes (its leaves). A decision stump makes a prediction based on the value of just a single input feature. Sometimes they are also called 1-rules. Depending on the type of the input feature, several variations are possible. For nominal features, one may build a stump which contains a leaf for each possible feature value or a stump with the two leaves, one of which corresponds to some chosen category, and the other leaf to all the other categories. For binary features these two schemes are identical. A missing value may be treated as a yet another category. For continuous features, usually, some threshold feature value is selected, and the stump contains two leaves — for values below and above the threshold. However, rarely, multiple thresholds may be chosen and the stump therefore contains three or more leaves. Decision stumps are often used as components (called "weak learners" or "base learners") in machine learning ensemble techniques such as bagging and boosting. For example, a Viola–Jones face detection algorithm employs AdaBoost with decision stumps as weak learners. The term "decision stump" was coined in a 1992 ICML paper by Wayne Iba and Pat Langley. (Wikipedia).
From playlist Decision Tree Learning
Decision trees are powerful and surprisingly straightforward. Here's how they are grown. Code: https://github.com/brohrer/brohrer.github.io/blob/master/code/decision_tree.py Slides: https://docs.google.com/presentation/d/1fyGhGxdGcwt_eg-xjlMKiVxstLhw42XfGz3wftSzRjc/edit?usp=sharing PERM
From playlist Data Science
undergraduate machine learning 31: Decision trees
Decision trees. The slides are available here: http://www.cs.ubc.ca/~nando/340-2012/lectures.php This course was taught in 2012 at UBC by Nando de Freitas
From playlist undergraduate machine learning at UBC 2012
(ML 2.1) Classification trees (CART)
Basic intro to decision trees for classification using the CART approach. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA
From playlist Machine Learning
Heap Sort - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
AdaBoost is one of those machine learning methods that seems so much more confusing than it really is. It's really just a simple twist on decision trees and random forests. NOTE: This video assumes you already know about Decision Trees... https://youtu.be/_L39rN6gz7Y ...and Random Forests
From playlist StatQuest
AdaBoost : Data Science Concepts
How do we put together lots of weak models into a STRONG model?
From playlist Data Science Concepts
Decision trees - A friendly introduction
A video about decision trees, and how to train them on a simple example. Accompanying blog post: https://medium.com/@luis.serrano/splitting-data-by-asking-questions-decision-trees-74afed9cd849 Helper videos: - Gini index: https://www.youtube.com/watch?v=u4IxOk2ijSs - Entropy and informat
From playlist Supervised Learning
AdaBoost (Adaptive Boosting) ensemble learning technique for classification
From playlist cs273a
[Machine Learning] Random Forest
explain random forest and compare with decision tree with visualization. all machine learning youtube videos from me, https://www.youtube.com/playlist?list=PLVNY1HnUlO26x597OgAN8TCgGTiE-38D6 all machine learning youtube videos from me, https://www.youtube.com/playlist?list=PLVNY1HnUlO26x
From playlist Machine Learning
AdaBoost in Python - Machine Learning From Scratch 13 - Python Tutorial
Get my Free NumPy Handbook: https://www.python-engineer.com/numpybook In this Machine Learning from Scratch Tutorial, we are going to implement the AdaBoost algorithm using only built-in Python modules and numpy. AdaBoost is an ensemble technique that attempts to create a strong classifie
From playlist Machine Learning from Scratch - Python Tutorials
Ensembles (3): Gradient Boosting
Gradient boosting ensemble technique for regression
From playlist cs273a
Boosting Machine Learning Tutorial | Adaptive Boosting, Gradient Boosting, XGBoost | Edureka
** Machine Learning Certification Training using Python: https://www.edureka.co/python ** This Edureka session will help you understand all about Boosting Machine Learning and boosting algorithms and how they can be implemented to increase the efficiency of Machine Learning models. The fol
From playlist Machine Learning Algorithms in Python (With Demo) | Edureka
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston Can multiple weak classifiers be used to make a strong one? We examine the boosting algorithm, which adjusts the weight of each classifier, and work through the
From playlist MIT 6.034 Artificial Intelligence, Fall 2010
(ML 11.4) Choosing a decision rule - Bayesian and frequentist
Choosing a decision rule, from Bayesian and frequentist perspectives. To make the problem well-defined from the frequentist perspective, some additional guiding principle is introduced such as unbiasedness, minimax, or invariance.
From playlist Machine Learning
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