What Is the Gini Index? The Gini index, or Gini coefficient, is a measure of the distribution of income across a population developed by the Italian statistician Corrado Gini in 1912. It is often.. In economics, the Gini coefficient (/ ˈ dʒ iː n i / JEE-nee), sometimes called the Gini index or Gini ratio, is a measure of statistical dispersion intended to represent the income inequality or wealth inequality within a nation or any other group of people. It was developed by the Italian statistician and sociologist Corrado Gini.. The Gini coefficient measures the inequality among values.
A common measure of wealth inequality is the Gini coefficient (or Gini index), G. A small value of G indicates a more equal wealth distribution with G = 0 corresponding to complete equality. In contrast, G = 1 corresponds to one person having all the wealth The Gini Index assigns income inequality a value ranging from 0 to 1, which reflects the nature of income distribution in a given region (Sometimes the Gini coefficient is represented as a percentage or an index, in which case it would be equal to (A/(A+B))x100%.) As stated in the Lorenz curve article, the straight line in the diagram represents perfect equality in a society, and Lorenz curves that are further away from that diagonal line represent higher levels of inequality
What is the traditional definition of Gini index? The Gini index or Gini coefficient is a statistical measure of distribution which was developed by the Italian statistician Corrado Gini in 1912. It is used as a gauge of economic inequality, measuring income distribution among a population Gini Index Intuition: Let's start with Gini Index, as it's a bit easier to understand. According to Wikipedia, the goal is to measure how often a randomly chosen element from the set would be incorrectly labeled. To visualize this, let's go back to the gumball examples The Gini coefficient, sometimes called the Gini Index or Gini ratio, is a statistical measure of distribution intended to represent the income or wealth distribution of a nation. The Gini coefficient was developed by Italian statistician Corrado Gini in 1912 and is the most commonly used measurement of wealth or income inequality The Gini coefficient (Gini index or Gini ratio) is a statistical measure of economic inequality in a population. The coefficient measures the dispersion of income Remuneration Remuneration is any type of compensation or payment that an individual or employee receives as payment for their services or the work that they do for an organization or.
The Gini index tells us how impure a node is, e.g. if all classes have the same frequency, the node is impure, if only one class is present, it is maximally pure. Variance and Gini index are minimized when the data points in the nodes have very similar values for y The Gini index is one of the most commonly used indicators of income inequality, and its computation and interpretation require a thorough understanding of various quantitative literacy concepts. In this article, we describe a unit for an interdisciplinary quantitative literacy course at a community college tha Gini Coefficient is also known as the Gini index is the statistical measure which is used in order to measure the distribution of the income among the population of the country i.e., it helps in measuring the inequality of income of the country's population. It is a value between 0 and 1 A note on the calculation and interpretation of the Gini index. Author links open overlay panel Robert I. Lerman Shlomo Yitzhaki. Abstract. We derive a convenient way to calculate the Gini coefficient, using the covariance. Our approach improves on accuracy since, unlike other approaches, it requires no aggregation. We also point out some. Introduction Interest in the Gini index among economists centers on its applications in the income distribution field. The Gini has a natural geometric interpretation as 1 minus twice the area between the Lorenz curve and the diagonal line representing perfect equality
Summary: The Gini Index is calculated by subtracting the sum of the squared probabilities of each class from one. It favors larger partitions. Information Gain multiplies the probability of the class times the log (base=2) of that class probability. Information Gain favors smaller partitions with many distinct values The Gini record or Gini coefficient is a factual proportion of dispersion created by the Italian analyst Corrado Gini in 1912. It is regularly utilized as a check of monetary imbalance, estimating pay appropriation or, less ordinarily, riches dissemination among a populace A clear interpretation of the absolute values of variable importance is hard to do well. GINI: GINI importance measures the average gain of purity by splits of a given variable. If the variable is useful, it tends to split mixed labeled nodes into pure single class nodes A new redistribution interpretation and an existing redistribution interpretation of the Gini are presented and applied to the concentration index. Both indicate the share of the total amount of any variable that needs redistributing in a particular way from rich to poor (or vice versa) to achieve a concentration index equal to zero
The degree of gini index varies from 0 to 1, Where 0 depicts that all the elements be allied to a certain class, or only one class exists there. The gini index of value as 1 signifies that all the elements are randomly zdistributed across various classes, and. A value of 0.5 denotes the elements are uniformly distributed into some classes If the order resulting from sorting by the socioeconomic and health variables are the same, the concentration index will have the same absolute value as the Gini coefficient. Following is an example of calculation of Gini Coefficient using infant mortality rates from 5 countries of the Andean area in 1997 (PAHO, Basic indicators brochure 1998) The easiest intuitive interpretation of the Gini coefficient invokes the Lorenz curve, as we will explore below. Rarely one sees the Gini coefficient being motivated from an individual-level type of discussion, although this is entirely possible to do The Gini statistic is a single number that represents the area under the cumulative lift chart relative to the area under a uniform distribution. The value's association with the cumulative lift represents the cumulative percentage of responses
The Gini Index is twice the area between the line of inequality y = x and the Lorenz curve y = L(x): GI = 2 Z 1 0 [x L(x)]dx: (This is twice the grey area in the previous picture.) The Gini Index is always between 0 and 1. A smaller number means that there is less inequality in income distribution Further notes on data quality and interpretation. In the chart above, we've used used the Gini index, as estimated from household survey data, as our measure of inequality. In this section we address a number of downsides to this approach. Our main reason for using the Gini index was the wide range of countries for which it is available The Gini coefficient, or Gini index, is a statistical measure of economic inequality and wealth distribution among a population. A value of zero represents perfect economic equality, and a value of.. The Gini coefficient or index is a prominent measure of income inequality. It leverages a scale of 0 to 1 to derive deviation from perfect income equality. A Gini index of 0 would imply perfect income equality, while an index of 1 would imply complete income disparity. The World Bank is the main organisation that provides the Gini index data
The Gini Index is the indicator par excellence, used to measure the level of distribution of monetary income and derived from social inequality I have a data set where each case represents a district, or unit, in a city. For each unit, I have the overall population, as well as the population of a particular minority group. I would like to calculate the Gini index of similarity for this city, where Gini is a measure of segregation that was described by Massey & Denton (1988). How can I calculate this Gini coefficient in SPSS Let's consider the simplest decision tree: A single if-else statement. Say, we want to predict someone's gender, given their height. We have the data for 10 people. It's pretty naive to do this but assume that's all we've got. This is our data (bo..
interpretation as the area between the 45 degree curve (which indicates perfect equality) and the Lorenz curve. yˆ 2 The Gini coefficient has the advantage of being invariant with respect to scale, so that larger areas or richer areas do not necessarily have larger or smaller Gini coefficients Gini coefficient is very similar to CAP but it shows proportion (cumulative) of good customers instead of all customers. It shows the extent to which the model has better classification capabilities in comparison to the random model. It is also called Gini Index. Gini Coefficient can take values between -1 and 1 The Gini coefficient or Somers' D statistic gives a measure of concordance in logistic models. It is a rank based statistic, where all results are paired (all observed with all predicted). In linear regression, it is a transformation of the Pearson correlation coefficient. Here is a nice paper that covers a lot of what is buried in the SGF paper
Gini index, a quantified representation of a nation's Lorenz curve. A Gini index of 0% expresses perfect equality, while index of 100% expresses maximal inequality A note on the calculation and interpretation of the Gini index | Semantic Scholar Abstract We derive a convenient way to calculate the Gini coefficient, using the covariance. Our approach improves on accuracy since, unlike other approaches, it requires no aggregation. We also point out some intuitive ways of interpreting the Gini
The Gini coefficient (Gini index or Gini ratio) is a statistical measure of economic inequality in a population. The coefficient measures the dispersion of income or distribution of wealth among the members of a populatio shares and shares, and Gini coefficient, allowing for probability weights and for complex survey design more generally. See also Zheng (2002) . • svylorenz variance estimates: - Cumulative shares and Gini: Kovacevic and Binder (1997) - Quantile group shares: Beach and Kaliski (1986) result relating variance The well-known Gini index is a rst answer. But in this paper, we shall go beyond that index and introduce and promote the use of a two-parameter index, which is derived from a two-parameter family of functions used to model the Lorenz curve of incomes. This family was rst proposed in [ 11 ], but our derivation, interpretation, and analysis is. The Gini index for the household income distribution in the United States in 2010 was estimatedtobe0.469(DeNavas-Walt,Proctor,andSmith2011). Although the Gini index is mostly used to measure the un-equal allocation of income, its area of application is very wide, ranging from computer science to ecology or industrial concen-tration. Thus, both.
The Gini coefficient, also known as the Gini Index, is widely used across the world. It is one of the most efficient and easily understood figures on inequality, which makes it easier to compare countries. At the same time, it does have its drawbacks which we will look at later In this code-heavy tutorial, learn how to build a logistic classification model in H2O using the prostate dataset to calculate AUC and GINI model metrics 3.2.1 Generalized Gini index The Gini index has the following interesting interpretation. Suppose an object is selected at random from one of C classes according to the probabilities (p 1;p 2;:::;p C) and is randomly assigned to a class using the same distribution. The probability of misclassi cation is X i X j6=i p ip j= X i X j p ip j X i p2. We provide a new way of interpreting the Gini correlation in the income source decomposition of the Gini index as a linear function of the ratio of the concordance pseudo-Gini (which measures the degree of agreement between the ranking of the incomes within a particular source and the ranking with respect to total income) to the within-source Gini (which measures income disparities within a particular income source) Gini Impurity. Gini Impurity is a measurement of the likelihood of an incorrect classification of a new instance of a random variable, if that new instance were randomly classified according to the distribution of class labels from the data set.. Gini impurity is lower bounded by 0, with 0 occurring if the data set contains only one class.. The formula for calculating the gini impurity of a.
Pakistan GINI index was 36.2 % in 2018, down by 0.00% from the previous year. Gini index measures the extent to which the distribution of income or consumption expenditure among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting. A problem arises when the Gini coefficient we are talking about is actually a Gini coefficient but in a corrected version, so called Corrected Gini coefficient [2]. It is the summary statistic of. WEKA (Waikato Environment for Knowledge Analysis) is a machine learning workbench that allows for quick experimentation with different algorithms and settings as well as preprocessing tools.Standard methods (regression, classification, clustering, association rule mining and attribute selection) can be loaded and configured in Weka Explorer without writing a single line of code Gini Index Geometric Interpretation It corresponds to one minus two times the area between the Lorenz curve and the line of perfect equality area, or the ratio of areas A/A+B indicated in the graph below. Gini Index - The direct calculation of the Gini Index from the Lorenz Curve demonstrated below in another advantage and explanation for its.
pressions of selected indices for normally distributed data, namely, the Gini index and Lift in the case of the common variance of scores, and the mean difference D, the KS, the Gini index, Lift, and information statistics in the general case, i.e., without assuming equality of variances. The normality of scores has to be tested Gini coefficient of zero expresses perfect equality, for example, when all the residents has the same income. On the other hand, Gini coefficient of 1 (100%) express maximall inequality among values, for example when one person has all the income. This number is calculated based on Lorentz curve Menu location: Analysis_Nonparametric_Gini Coefficient of Inequality This method calculates the Gini coefficient (G) of inequality with bootstrap confidence intervals. A Lorenz plot is produced when a single variable is specified for analysis, otherwise the summary statistics alone are displayed for a group of variables
The Gini Index in Practice: A Global Example. There are several estimates for the Gini coefficient worldwide. For instance, around the time of the Great Recession, in 2008, scholars Branko Milanovic and Christoph Lakner surmise that the global Gini coefficient for income was 0.705. This is a moderate decrease from 1988's estimated coefficient. Gini index G, let's start with a model population of 100 people with a total wealth of $100. Example 1. Suppose 20 people have $5 each. Draw the Lorenz curve and calculate G. Since 80% of the population has zero wealth, L(x) = 0, 0 ≤ x ≤ 0.80. Then it is a straight line from the point (.80,0) to (1,1). The area o A Gini coefficient of 100 represents 100 percent concentration in a country's income distribution. In a country with a Gini of 100, one person receives all of the country's income. Everyone else gets nothing.[pullquote]The official Gini coefficient for the United States has shot way up from the all-time low set in 1968.[/pullquote GINI Different ROC curves interpretation. The Gini index or coefficient is a way to adjust the AUC so that it can be clearer and more meaningful. It's more natural for us to see a perfectly random model having 0, reversing models with a negative sign and the perfect model having 1. The range of values now is [-1, 1]
Generally, your performance will not change whether you use Gini impurity or Entropy. Laura Elena Raileanu and Kilian Stoffel compared both in Theoretical comparison between the gini index and information gain criteria. The most important remarks were: It only matters in 2% of the cases whether you use gini impurity or entropy The Gini coefficient measures how far the actual Lorenz curve for a society's income or wealth is from the line of equality. Both the Lorenz curve and the line of equality are plotted on a graph. The practi cal meaning of Gini coeﬃ cients by Malte Luebker The so-called Gini coeﬃ cient (or Gini-Index) has become by far the most popular measure for inequality since it was ﬁ rst introduced by the Italian stati sti cian Corrado Gini (1884-1965) almost a century ago. It summarizes the extent of inequality in a single ﬁ gure I used ineqdec0 to calculate the Gini Index for time children are reported to spend with their parents (mother/father). Beyond giving an insight about the level of inequality within and across groups, how can I interpret, for instance, a gini Index of 0.5 or 0.3 (e.g., when related to income distribution, the interpretation is made in terms of. India not only has one of the highest levels of inequality in the region, but it also shows very large increases in inequality since 1990. Its net Gini index of inequality (based on income net of taxes and transfers) rose from 45.18 in 1990 to 51...
A Gini Coefficient can help a lender (or investor) understand how good the lender's credit model is at predicting who will repay and who will default on a loan. The GINI co-efficient compares the Lorenz curve (the cumulative distribution) with the line of perfect randomness. Graphically illustrated, the Gini is the ratio of the area. Analytical Interpretation The Gini is an inequality index, corresponding to the ratio between the average of the absolute deviations of the incomes of all the people in the sample and twice the average. Once there are (−1) 2 distinct pairs of people in the sample, Gini's formula is: = 1 (−1 The Gini Coefficient - Measuring Inequality The Gini coefficient is a value ranging from 0 to 1 which measures inequality. 0 represents perfect equality - i.e everyone in a population has exactly the same wealth. 1 represents complete inequality - i.e 1 person has all the wealth and everyone else has nothing The most widely used measure for studying social, economic, and health inequality is the Gini index/ratio. Whereas other measures of inequality possess certain useful characteristics, such as the straightforward decomposability of the generalized entropy measures, the Gini index has remained the most popular, at least in part due to its ease of interpretation
Its proper use and interpretation of income Gini coefficient is controversial. As example, Mellor explains, income Gini index of developing countries can rise, that is the income distribution get more unequal at the same time that the number of people in absolute poverty are reduced substantially 43. Gini Index Applications Find and interpret the Gini index Question Consider the Gini index, defined as G.I = 200 x = f(x)dr for 0 53.f(x) 1. What is the geometric interpretation for the Gini index? Assume that AL = the area bound between y = x and y = f(x), and Ap = the area bound between the line of perfect equality and the x axis A Gini index based on individual incomes is different to a Gini index based on household incomes for the same country. As a result, the rankings of countries change depending on whether the index is based on household incomes or individual incomes, creating some subjectivity in its use and interpretation
Lorenz curve coincides with the diagonal in Figure 2) and 1 perfect inequality. If the Gini coefficient for some variable (e.g., income) in a country has increased over time, it means that the distribution of that variable among the population has become more unequal. Similarly, if the Gini coefficient four, say Gini index. Another decision tree algorithm CART (Classification and Regression Tree) uses the Gini method to create split points. Where, pi is the probability that a tuple in D belongs to class Ci. The Gini Index considers a binary split for each attribute. You can compute a weighted sum of the impurity of each partition 12. What is Gini Coefficient? Gini Coefficient is an indicator of how well the model outperforms random predictions. It can be computed from the area under the ROC curve using the following formula: Gini Coefficient = (2 * AUROC) - 1. 13. Conclusion. Clearly, just the accuracy score is not enough to judge the performance of the models The Gini Coefficient ranges between 0 and 1 (or it can also be expressed as a number from 0 to 100) and is given by the ratio of the areas: If A = 0, it means the Lorenz Curve is actually the Line of Equality. In this case, the Gini Coefficient is 0 and it means there is perfect distribution of income (everyone earns the same amount) 43. Gini Index Applications Find and interpret the Gini index Question Consider a Lorenz curve given by f(x) = x for 0 <x<1 and n 2 1. What happens to the Gini index G. I as n - 0, and what's the interpretation of that result? Select the correct answer below: OAsn00, G. I0, denoting complete income inequality. As n00, G
The Gini coefficient is a popular metric on Kaggle, especially for imbalanced class values. But googling Gini coefficient gives you mostly economic explanations ones have been the Herfindahl-Hirschman Index (HHI) and the Gini-Coefficient (GC is an inequality measure rather than a concentration one. Most of these measures where ) proposed to study industry concentration and market competition during the 70's and 80's. 4 in this video we're going to discuss income inequality which is something that is often debated thinking about comparing countries thinking about whether it's an issue or not and how to address it and to appreciate what income inequality is let's imagine two different countries let's imagine first country a and there's two people in country a so you have person one here who makes $1000 a year. Explaining the differences between gini index and information gain is beyond this short tutorial. But I have written a quick intro to the differences between gini index and information gain elsewhere. Choosing between the gini index and information gain is an analysis all in itself and will take some experimentation. Minsplit, Minbucket, Maxdept
Bank data, between 1981 and 2013, the Gini index ranged between 0.3 and 0.6 worldwide. The coefficient allows direct comparison of two populations' income distribution, regardles Gini Index. It is the name of the cost function that is used to evaluate the binary splits in the dataset and works with the categorial target variable Success or Failure. Higher the value of Gini index, higher the homogeneity. A perfect Gini index value is 0 and worst is 0.5 (for 2 class problem) We generalize the Gini coefficient to ordinal categorical variables. In particular, our generalized Gini coefficient provides an alternative way to measure concentration when only stratified data are available. JEL Classification: D30, D63, I31 cover a variety of situations where wages, mixed incomes and capital incomes have a varying importance. The interval q0.90 − q0.95 concerns what he calls the middle class, formed mainly by salaried executives, which thus in fact corresponds more to the higher middle class A simple way to calculate the Gini coefficient, and some implications Branko Milanovic* World Bank, Washington, D.C., USA Received 7 October 1996; accepted 19 March 1997 Abstract Tile paper proposes a new, and a much simpler, way to calculate the Gini coefficient. The existence of a relationshi