Decision trees | Classification algorithms
In information theory and machine learning, information gain is a synonym for Kullback–Leibler divergence; the amount of information gained about a random variable or signal from observing another random variable. However, in the context of decision trees, the term is sometimes used synonymously with mutual information, which is the conditional expected value of the Kullback–Leibler divergence of the univariate probability distribution of one variable from the conditional distribution of this variable given the other one. The information gain of a random variable X obtained from an observation of a random variable A taking value is defined the Kullback–Leibler divergence of the prior distribution for x from the posterior distribution for x given a. The expected value of the information gain is the mutual information of X and A – i.e. the reduction in the entropy of X achieved by learning the state of the random variable A. In machine learning, this concept can be used to define a preferred sequence of attributes to investigate to most rapidly narrow down the state of X. Such a sequence (which depends on the outcome of the investigation of previous attributes at each stage) is called a decision tree and applied in the area of machine learning known as decision tree learning. Usually an attribute with high mutual information should be preferred to other attributes. (Wikipedia).
From playlist Decision Tree Learning
Introduction to Decision Trees | Decision Trees for Machine Learning | Part 1
The decision tree algorithm belongs to the family of supervised learning algorithms. Just like other supervised learning algorithms, decision trees model relationships, and dependencies between the predictive outputs and the input features. As the name suggests, the decision tree algorit
From playlist Introduction to Machine Learning 101
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
Decision Tree 4: Information Gain
Full lecture: http://bit.ly/D-Tree After a split, we end up with several subsets, which will have different values of entropy (purity). Information Gain (aka mutual information) is an average of these entropies, weighted by the size of each subset.
From playlist Decision Tree
Value of Information in the Earth Sciences
Overview, narrated by Tapan Mukerji Eidsvik, J., Mukerji, T. and Bhattacharjya, D., 2015. Value of information in the earth sciences: Integrating spatial modeling and decision analysis. Cambridge University Press.
From playlist Uncertainty Quantification
(IC 1.6) A different notion of "information"
An informal discussion of the distinctions between our everyday usage of the word "information" and the information-theoretic notion of "information". A playlist of these videos is available at: http://www.youtube.com/playlist?list=PLE125425EC837021F Attribution for image of TV static:
From playlist Information theory and Coding
Decision Tree 8: Random Forests
Full lecture: http://bit.ly/D-Tree Decision trees are compact and extremely fast at testing time. They can also handle missing values, and irrelevant attributes naturally. On the downside, they are restricted to axis-aligned splits of the data, and the algorithm is not guaranteed to find
From playlist Decision Tree
Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka
** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training ** This Edureka video on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topi
From playlist Brief Introduction to Data Science
Decision Tree Classification in Python (from scratch!)
This video will show you how to code a decision tree classifier from scratch! #machinelearning #datascience #python For more videos please subscribe - http://bit.ly/normalizedNERD Join our discord - https://discord.gg/39YYU936RC Source code - https://github.com/Suji04/ML_from_Scratch
From playlist Tree-Based Algorithms
Learning To See [Part 15: Information]
In this series, we'll explore the complex landscape of machine learning and artificial intelligence through one example from the field of computer vision: using a decision tree to count the number of fingers in an image. It's gonna be crazy. Supporting Code: https://github.com/stephencwe
From playlist Learning To See
Decoding the Science of Decision Trees! Learn from Experts | Webinar -1 | Edureka
Watch Sample Class recording: http://goo.gl/OBlNnC This course is designed for professionals who aspire to learn 'R' language for Analytics. The course starts from the very basics like: Introduction to R programming, how to import various formats of Data, manipulate it, etc. to advanced t
From playlist Webinars by Edureka!
Data Science - Part V - Decision Trees & Random Forests
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniq
From playlist Data Science
Clas - 5 Data Science Training | Decision Tree Classifier Explained | Edureka
(Edureka Meetup Community: http://bit.ly/2KMqgvf) Join our Meetup community and get access to 100+ tech webinars/ month for FREE: http://bit.ly/2KMqgvf Topics to be covered in this session: 1. Introduction To Classification Algorithms 2. What Is a Decision Tree? 3. Understanding Decision
From playlist Data Science Training Videos | Edureka Live Classes
Decision Tree 6: degenerate splits and gain ratio
Full lecture: http://bit.ly/D-Tree Information Gain can be biased towards attributes with a very large number of values (tiny subsets tend to be pure). This can lead to degenerate trees that cannot generalise at all. We avoid the problem by normalising the Information Gain with the entrop
From playlist Decision Tree
[Machine Learning] Decision Tree - How Decision Tree Works?
Short Video tutorial to understand Decision Tree in Machine Learning. Subtitle (English) is also available, please click 'CC' button for subtitle. all machine learning youtube videos from me, https://www.youtube.com/playlist?list=PLVNY1HnUlO26x597OgAN8TCgGTiE-38D6
From playlist Machine Learning