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Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning.In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations.
A classification model (classifier or diagnosis [7]) is a mapping of instances between certain classes/groups.Because the classifier or diagnosis result can be an arbitrary real value (continuous output), the classifier boundary between classes must be determined by a threshold value (for instance, to determine whether a person has hypertension based on a blood pressure measure).
In machine learning, this concept can be used to define a preferred sequence of attributes to investigate to most rapidly narrow down the state of X. Such a sequence (which depends on the outcome of the investigation of previous attributes at each stage) is called a decision tree , and when applied in the area of machine learning is known as ...
In economics, the Gini coefficient (/ ˈ dʒ iː n i / JEE-nee), also known as the Gini index or Gini ratio, is a measure of statistical dispersion intended to represent the income inequality, the wealth inequality, or the consumption inequality [2] within a nation or a social group. It was developed by Italian statistician and sociologist ...
The range of the Gini index is between 0 and 1 (0% and 100%), where 0 indicates perfect equality and 1 (100%) indicates maximum inequality. The Gini index is the most frequently used inequality index. The reason for its popularity is that it is easy to understand how to compute the Gini index as a ratio of two areas in Lorenz curve diagrams ...
C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. [1] C4.5 is an extension of Quinlan's earlier ID3 algorithm.The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier.
The Gini-Simpson Index is also called Gini impurity, or Gini's diversity index [14] in the field of Machine Learning. The original Simpson index λ equals the probability that two entities taken at random from the dataset of interest (with replacement) represent the same type.
The observations are ranked from the greatest to the least value of the index. There are 31 thresholds, consisting of the 30 values of the index and one additional threshold that is greater than all the index values, which creates the point at the origin (0,0). Each point is labeled to indicate the value of each threshold.