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Classification and regression Decision Trees in Python

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DecTree

DecTree is the python implementation of a decision tree which can be used for predictions of both classification and regression cases in machine learning context. DecTree classifier is capable of handling both numerical and categorical attributes.

Structure

Decision tree is implemented using the object-oriented structure where each sub-tree is generated recursively as a property of its parent root node. This linked recursive structure is then used for traversals accordingly in order to predict for new cases.

Several sample data-sets are provided in data folder which are used for validating the decision tree implementation. Accuracy, root mean square error and R-square error are reported for each case along with optional graphs.

Configurations

Minimum threshold for the entropy validation (used for termination of recursions thus creating a leaf node) and maximum allowed depth of the tree can be configured using the configuration values. Please note that lower thresholds and higher depths can lead to an over-fitted model.

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Classification and regression Decision Trees in Python

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