What Classification trees is
Classification trees are a type of predictive modeling technique used to identify which group a data point belongs to. Classification trees are built by splitting the data into subsets based on the values of one or more predictor variables. The goal is to split the data in such a way that points in the same group (or class) share similar characteristics.
Steps for Building a Classification Tree:
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Select the predictor variable(s) to split the data on.
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Calculate the significance of each predictor variable and select the one with the highest significance.
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Partition the data based on the values of the chosen predictor variable.
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For each partition, calculate the significance of the other predictor variables.
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Select the predictor variable with the highest significance and split the data based on its values.
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Continue this process until all of the data points are in distinct groups.
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Generate a tree diagram to visualize the resulting partitions.
Examples
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Classification trees are used in statistics to identify customer segments based on their demographic characteristics (age, gender, income, etc.).
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Classification trees are used in medical research to predict the likelihood of a patient developing a specific disease based on a variety of factors (genetic, environmental, etc.).
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Classification trees are used in credit scoring to determine a person’s credit worthiness based on their financial history.
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Classification trees are used in marketing to determine the best target audience for a particular product or service based on customer preferences.