#DECISION TREE ENTROPY SERIES#

Is Entropy Different Than Information Gain?Įntropy is different from information gain since information gain uses entropy as part of its calculation to decide which node to make a split. Where the pi is the probability of randomly picking one element of that specific class (a proportion that class makes up of the dataset) If we had a dataset that was 50% “No” and 50% “Yes,” this dataset would have high entropy. If we have a dataset (like in fraud detection) that is mostly data from situations that isn’t fraudulent, this dataset would have low entropy.

This is because Gini Index measures a categorical variable’s impurity (variance), and the Gini Coefficient measures a numerical variable’s inequality (variance), usually income.ĭue to this subtle difference, some fields have started to use the terms interchangeably, making the situation quite confusing for others!Įntropy is simply “how much variance the dataset has.” Outside of data science and machine learning, commonly in the field of economics, these variables start to “blend together.” Why Is The Gini Index seen as the Gini Coefficient in fields outside of data science? The Gini Coefficient (in machine learning) is a binary classification ranking method that depicts how likely a variable is to be positive. While many commonly confuse this, the Gini index is a classification measure measuring the level of purity at each node (how much does it classify). In the study of economics, many will use these terms interchangeably, but within data science and decision trees, these terms are not the same. The Gini index is not the same as the Gini Coefficient in data mining or machine learning. Is The Gini Index Different from Gini Coefficient? Since these mean the same thing, you’d calculate them the same way. Both of these are commonly referenced as splitting criteria that are used in decision trees. There is no difference between the Gini index and Gini impurity. Is The Gini Index Different from Gini Impurity? The Gini index will always be between, where 0 is a selection that perfectly splits each class in your dataset (pure), and 0.5 means that neither of the classes was correctly classified (impure). Where each random p i is our probability of that point being randomly classified to a certain class. When your decision tree has to make a “split” in your data, it makes that split at that particular root node that minimizes the Gini index.īelow, we can see the Gini Index Formula: The Gini Index is simply a tree-splitting criterion.

#DECISION TREE ENTROPY SERIES#

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