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.
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.
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.