Gini index in data mining ppt

<p>When comparing Gender, Car Type, and Shirt Size using the Gini Index, Car Type would be the better attribute.</p>

A Gini Index of 0.5 denotes equally distributed elements into some classes.

Gain Ratio, Gini Index, Binary Split, Discrete-Valued Attributes, Information Gain, Gain Ratio, Gini Gain Ratio-Data Warehousing and Data Mining-Book Summary Part 05-Computer Microsoft PowerPoint - lesson4-Classification-2. pptx.

Usually, the given data set is divided into training and test sets, with training set used to Gini index. Entropy. Misclassification error. Jeff Howbert Introduction to. Three impurity measures, resubstitution-error, gini-index and the en- tropy, for splitting data will be discussed in Section 2.2.1. The actual split- ting and tree. Comparative Study of CART and C5.0 using Iris Flower Data. . What is Classification in Data Mining. A binary tree using GINI Index as its splitting criteria.

Splits into two. Gini Index Entropy Misclassification error. M0. Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation -. lecture notes for chapter 4. The Gini coefficient is a measure of inequality of a distribution. It is defined data.

This large amount of data can be helpful for analyzing and extracting useful knowledge from it.

If the population mean and boundary values for each interval are also known,. Gini index is the most commonly used measure of inequality. Also referred as Gini ratio or Gini coefficient. Gini index for binary variables is calculated in the example below. Now we will calculate Gini index of student and inHostel. Figure gives a decision tree for the training data data. The calculations that Nick Cox gave are absolutely correct when computing the Gini index of the features, and help give us information about the features and their homogeneity. The hidden patterns of data are analyzed and then categorized into useful knowledge.

The Gini in-dex has been used in various works such as (Breiman et al., 1984) and (Gelfand et al., 1991) and it is defined as: Gini(y;S) = 1¡ X cj2dom(y) ˆfl fl¾ y=cjS fl fl jSj!2.

This process is known as Data Mining. A Gini coefficient of one (100 on the percentile scale) expresses maximal inequality among values (for example where only one person has all the income) this answer in from Wikipedia can any one explain me in simple way. what is the use of it in data mining. This video is the simplest hindi english explanation of gini index in decision tree induction for attribute selection measure. Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar. Formula for Gini Index. where p i is the probability of an object being classified to a particular class. Split Tables CART Splitting Criteria: Gini Index If a data set T contains examples from n classes, gini index, gini(T) is defined as where pj is the relative frequency of class j in T. gini(T) is minimized if the classes in T are skewed. Data Mining.

The Gini Index takes into consideration the distribution of the sample with zero reflecting the most distributed sample set. Out of the three listed attributes, Car Type has the lowest Gini Index. Scan the data set and 2.

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