This video is the simplest hindi english explanation of gini index in decision tree induction for attribute selection measure.
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Gini Index in Data Mining: Today, we will learn to calculate gain in Gini Index when splitting on A and B Attribute. Find out which attribute would the decision tree induction algorithm choose. Question: Consider the following data set for a binary problems. Table: Given in Video --Calculate the gain in the Gini index when splitting on A and B. Which Attribute would the decision tree induction algorithm choose? --Calculate the information gain when splitting on A and B. Which attribute would the decision tree induction algorithm choose ? Hope you Guys liked this video and found this helpful if yes so please Hit on SUBSCRIBE button. Check out our website : http://www.technofun.tk/ Don''t Forget To Check Out These Videos[You Gotta Watch These At-least Once] Most Recent Upload: https://goo.gl/7AaULr Most Popular Upload: https://goo.gl/5216JU I ***************Likes & Subscribe **************** ***************HELP US TO GROW*************** ***************SUPPORT NEEDED**************** Follow us on Facebook:- https://www/facebook.com/Techitechno Follow us on Instagram:- https://www.instagram.com/technofuns TAKE CARE YOU TUBERS & STAY BLESSED :)
Views: 1869 ᴛᴇᴄʜɴᴏғᴜɴ
How does a Decision Tree Work? A Decision Tree recursively splits training data into subsets based on the value of a single attribute. Splitting stops when every subset is pure (all elements belong to a single class) and OMG wow! I'm SHOCKED how easy it was .. No wonder others going crazy sharing this??? Share it with your other friends too! Code for visualising a decision tree - https://github.com/bhattbhavesh91/visualize_decision_tree Please Subscribe! That is the thing you could do that would make me happiest. You can find me on: GitHub - https://github.com/bhattbhavesh91 Medium - https://medium.com/@bhattbhavesh91
Views: 20909 Bhavesh Bhatt
-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
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Full lecture: http://bit.ly/D-Tree Which attribute do we select at each step of the ID3 algorithm? The attribute that results in the most pure subsets. We can measure purity of a subset as the entropy (degree of uncertainty) about the class within the subset.
Views: 174663 Victor Lavrenko
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Views: 9004 Artificial Intelligence - All in One
This video shows how to calculate the Gini Index that corresponds to a given Lorenz function, using either the fundamental theorem of calculus or the function integration (fnInt) command on a TI83 graphing calculator. The meaning of the Gini Index is also explained.
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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
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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.
Views: 152186 Victor Lavrenko
Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr https://goo.gl/ZpWYzt Books! Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ Follow Josh on Twitter: https://twitter.com/random_forests Check out more Machine Learning Recipes here: https://goo.gl/KewA03 Subscribe to the Google Developers channel: http://goo.gl/mQyv5L
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The challenge in the decision tree implementation is to identify which attributes do we need to consider as the root node and each level. Attributes selection is important here. We have different attributes selection measures to identify an attribute which can be considered as the root note at each level. Learn the importance of a good splitting criterion and attribute selection measure – entropy. Entropy calculation is explained in detail in the video. Entropy characterizes the purity/impurity of a variable. It’s an indicator of how messy your data is. For the detailed video tutorials, code files, data-sets and other material, please visit our site https://statinfer.com/ This video is part of the e-learning course - Machine Learning with Python (https://statinfer.com/course/machine-learning-with-python-2/)
Views: 855 Statinfer Analytics
In this video, I create a decision tree using Gini Impurity to determine the splitting attributes. I originally created this video (and the others in my series) to be used with a specific KDD class which is taught at my home university. I first encountered this algorithm in class there. If you would like to look into this topic in more detail, or read a bit about some similar algorithms, I am including the link to one of the presentations that I used as a reference. coitweb.uncc.edu/~ras/KBS-Class/1-Decision-Trees.ppt Thank you for watching!
Views: 45159 Laurel Powell
Full lecture: http://bit.ly/D-Tree A Decision Tree recursively splits training data into subsets based on the value of a single attribute. Each split corresponds to a node in the. Splitting stops when every subset is pure (all elements belong to a single class) -- this can always be achieved, unless there are duplicate training examples with different classes.
Views: 495286 Victor Lavrenko
The Gini coefficient is a measure of inequality of a distribution. It is defined as a ratio with values between 0 and 1: the numerator is the area between the Lorenz curve of the distribution and the uniform distribution line; the denominator is the area under the uniform distribution line. It was developed by the Italian statistician Corrado Gini and published in his 1912 paper "Variabilità e mutabilità" ("Variability and Mutability"). The Gini index is the Gini coefficient expressed as a percentage, and is equal to the Gini coefficient multiplied by 100. (The Gini coefficient is equal to half of the relative mean difference.)
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Chapter 8, week9 Data Mining - IT446
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My web page: www.imperial.ac.uk/people/n.sadawi
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In this video you will learn what is Gain chart and how is it constructed. You will also learn how to use gain chart in logistic regression for model monitoring Contact [email protected]
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CART, Classification and Regression Trees is a family of Supervised Machine Learning Algorithms. Follow this link for an entire Intro course on Machine Learning using R, did I mention it's FREE: https://www.youtube.com/playlist?list=PLjPbBibKHH18I0mDb_H4uP3egypHIsvMn Also, be sure to check out my channel for over 400 tutorials on Excel, R, Statistics, Machine Learning, basic Math, and more.
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Busi Calc Applications of Area Between Curves - Gini Index
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