Search results “Gini index calculation data mining”

This video is the simplest hindi english explanation of gini index in decision tree induction for attribute selection measure.

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Red Apple Tutorials

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 ?
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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)
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Code for visualising a decision tree -
https://github.com/bhattbhavesh91/visualize_decision_tree
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#decisiontree #Gini #machinelearning

Views: 27616
Bhavesh Bhatt

explanation of induction of decision tree using gini index in hindi

Views: 14574
Red Apple Tutorials

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amanj aladin

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Rawaz hassan

Decision Tree Classification Algorithm – Solved Numerical Question 1 in Hindi
Data Warehouse and Data Mining Lectures in Hindi

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Well Academy

In this video, I explained that how to find gini index of an attribute in data mining.

Views: 4560
DataMining Tutorials

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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Analytics University

Recorded with https://screencast-o-matic.com

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Purvaja Balaji

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Paul Turner

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Last moment tuitions

In this video, I explained that how to find gain ratio of an attribute in data mining.

Views: 4757
DataMining Tutorials

Views: 24393
Liam Malloy

کەمپینى بە کوردى کردنى زانست لە زانکۆى گەشە پێدانى مرۆیی

Views: 12716
shanga abdulla

This video explains the concept of Gini Coefficient using simple illustrations.

Views: 25315
Singapore Department of Statistics

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Saif Rahman

Views: 1205
John Sieben

Views: 5443
Lana luqman

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.
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Views: 19546
Udacity

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: 183576
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!
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Subscribe to the Google Developers channel: http://goo.gl/mQyv5L

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Google Developers

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Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
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Artificial Intelligence - All in One

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: 1332
Statinfer Analytics

Mohamed Mnete: Here I try my best to answer the question of what a decision tree is, how it is created, and how it is used. I explain this in the context of entropy and information gain.
Please LIKE, SHARE, SUBSCRIBE AND comment any questions you may have.
Live, Laugh, Study and Love!

Views: 231
Muddy Jeff

Example calculating the Gini index. This is an application of the area between curves

Views: 282
Christopher Vaughen

This video is a hindi explanation about attribute selection measure and describe about information gain in data mining

Views: 5825
Red Apple Tutorials

classification - decision tree induction. Information gain, gini index, entropy

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Pak Project

کەمپینی بە کوردی کردنی زانست لە زانکۆی گەشەپێدانی مرۆیی
Data Mining Classification Gini Index

Views: 1713
salim hasan

This video introduces the Gini coefficient, which is a way to summarize income inequality using a single number.
For more information and a complete listing of videos and online articles by topic or textbook chapter, see http://www.economistsdoitwithmodels.com/economics-classroom/
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jodiecongirl

Here I will describe about what is decision tree,how to implement decision tree model in R,how to plot roc curve in decision tree in R,implement decision tree using rpart,calculate auc in R,decision tree using rpart
#machinelearning #decisiontree #R

Views: 14763
Data Science by Arpan Gupta IIT,Roorkee

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What is GINI COEFFICIENT? What does GINI COEFFICIENT mean? GINI COEFFICIENT meaning - GINI COEFFICIENT definition -GINI COEFFICIENT explanation.
The Gini coefficient (also known as the Gini index or Gini ratio) is a measure of statistical dispersion intended to represent the income distribution of a nation's residents, and is the most commonly used measure of inequality. It was developed by the Italian statistician and sociologist Corrado Gini and published in his 1912 paper Variability and Mutability (Italian: Variabilita e mutabilita).
The Gini coefficient measures the inequality among values of a frequency distribution (for example, levels of income). A Gini coefficient of zero expresses perfect equality, where all values are the same (for example, where everyone has the same income). A Gini coefficient of 1 (or 100%) expresses maximal inequality among values (e.g., for a large number of people, where only one person has all the income or consumption, and all others have none, the Gini coefficient will be very nearly one). However, a value greater than one may occur if some persons represent negative contribution to the total (for example, having negative income or wealth). For larger groups, values close to or above 1 are very unlikely in practice. Given the normalization of both the cumulative population and the cumulative share of income used to calculate the Gini coefficient, the measure is not overly sensitive to the specifics of the income distribution, but rather only on how incomes vary relative to the other members of a population. The exception to this is in the redistribution of wealth resulting in a minimum income for all people. When the population is sorted, if their income distribution were to approximate a well known function, then some representative values could be calculated.
The Gini coefficient was proposed by Gini as a measure of inequality of income or wealth. For OECD countries, in the late 20th century, considering the effect of taxes and transfer payments, the income Gini coefficient ranged between 0.24 and 0.49, with Slovenia the lowest and Chile the highest. African countries had the highest pre-tax Gini coefficients in 2008–2009, with South Africa the world's highest, variously estimated to be 0.63 to 0.7, although this figure drops to 0.52 after social assistance is taken into account, and drops again to 0.47 after taxation. The global income Gini coefficient in 2005 has been estimated to be between 0.61 and 0.68 by various sources.
There are some issues in interpreting a Gini coefficient. The same value may result from many different distribution curves. The demographic structure should be taken into account. Countries with an aging population, or with a baby boom, experience an increasing pre-tax Gini coefficient even if real income distribution for working adults remains constant. Scholars have devised over a dozen variants of the Gini coefficient.

Views: 7988
The Audiopedia

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: 159768
Victor Lavrenko

Decision Tree Algorithm Part 2
https://youtu.be/ffZ0ShPi-wg
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Decision Tree Solved Example

Views: 27803
5 Minutes Engineering

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Shwan Barzan

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.

Views: 53259
Hands On Math

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Noureddin Sadawi

Also called Classification and Regression Trees (CART) or just trees.
R file: https://goo.gl/Kx4EsU
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Decision trees are an important tool for developing classification or predictive analytics models related to analyzing big data or data science.
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Bharatendra Rai

Blog post: https://medium.com/p/5810d35d54b4/

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Luis Serrano

http://www.t4tutorials.com/jaccard-coefficient-similarity-measure-for-asymmetric-binary-variables/
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University Of Shamil

This video is created by recording from http://data.worldbank.org/indicator/SI.POV.GINI/countries/1W-ID-US-PH?page=1&display=map
This is just a way to present it simpler

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