Gini index in data mining ppt

16 Sep 2015 Data Mining: Classification. the gini index of the split data contains examples from n classes, the gini index gini(T) is defined as The attribute  Introduction to Data Mining. 4/18/2004. 31. Measure of Impurity: GINI. ○ Gini Index for a given node t : (NOTE: p( j | t) is the relative frequency of class j at node t). to split the data, the Gini index for node N1 is 0.4898, and for node N2, it is 0.480. Web usage mining is the task of applying data mining techniques to extract.

The Gini coefficient is a measure of inequality of a distribution. It is defined data . 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. Step 1: Gini(X) = 1 – [(4/9) 2 + (5/9) 2 ] = 40/81. Figure gives a decision tree for the training data data. The splitting attribute at the root is pincode and the splitting criterion here is pincode = 500 046. Similarly, for the left child node, the splitting criterion is age < 48 (the p g g ( splitting attribute is age). 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.

Figure gives a decision tree for the training data data. The splitting attribute at the root is pincode and the splitting criterion here is pincode = 500 046. Similarly, for the left child node, the splitting criterion is age < 48 (the p g g ( splitting attribute is age).

The Gini coefficient is a measure of inequality of a distribution. It is defined data . 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. Step 1: Gini(X) = 1 – [(4/9) 2 + (5/9) 2 ] = 40/81. Figure gives a decision tree for the training data data. The splitting attribute at the root is pincode and the splitting criterion here is pincode = 500 046. Similarly, for the left child node, the splitting criterion is age < 48 (the p g g ( splitting attribute is age). 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.

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar

16 Sep 2015 Data Mining: Classification. the gini index of the split data contains examples from n classes, the gini index gini(T) is defined as The attribute  Introduction to Data Mining. 4/18/2004. 31. Measure of Impurity: GINI. ○ Gini Index for a given node t : (NOTE: p( j | t) is the relative frequency of class j at node t). to split the data, the Gini index for node N1 is 0.4898, and for node N2, it is 0.480. Web usage mining is the task of applying data mining techniques to extract. Measures of Node Impurity. ○ Entropy. ○ Gini Index. ○ Misclassification error. TNM033: Introduction to Data Mining. ‹#›. How to Find the Best Split. B? Yes. No.

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. Step 1: Gini(X) = 1 – [(4/9) 2 + (5/9) 2 ] = 40/81.

Measures of Node Impurity. ○ Entropy. ○ Gini Index. ○ Misclassification error. TNM033: Introduction to Data Mining. ‹#›. How to Find the Best Split. B? Yes. No. 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  data. ▫ Branches in the tree are attribute values. ▫ Leaf nodes are the class labels. □ Supervised examples from n classes, the gini index gini(T) is defined as.

Gini Index for Trading Volume = (7/10)0.49 + (3/10)0 = 0.34 From the above table, we observe that ‘Past Trend’ has the lowest Gini Index and hence it will be chosen as the root node for how decision tree works .

Comparative Study of CART and C5.0 using Iris Flower Data. 6. What is Classification in Data Mining? A binary tree using GINI Index as its splitting criteria.

A Gini Index of 0.5 denotes equally distributed elements into some classes. Formula for Gini Index. where p i is the probability of an object being classified to a particular class. While building the decision tree, we would prefer choosing the attribute/feature with the least Gini index as the root node. 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. with pace.Thus, the amount of data in the information industry is getting higher day by day. This large amount of data can be helpful for analyzing and extracting useful knowledge from it. The hidden patterns of data are analyzed and then categorized into useful knowledge. This process is known as Data Mining. Data Mining. When comparing Gender, Car Type, and Shirt Size using the Gini Index, Car Type would be the better attribute. 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. TNM033: Introduction to Data Mining ‹#› Continuous Attributes Several Choices for the splitting value – Number of possible splitting values = Number of distinct values n For each splitting value v 1. Scan the data set and 2. Compute class counts in each of the partitions, A < v and A v 3. Compute the entropy/Gini index 1 Data Mining: Concepts and Techniques (3rd ed.) — Chapter 8 — Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University ©2011 Han, Kamber & Pei.