HW 4
Description
To build a regression tree learner:
- Apply supervised Discretization
- At each level of the tree, break the data on the ranges and find the column whose breaks most reduces the variability of the target variable (we will use dom).
- For each break, apply the regression tree learner recursively.
- Recursion stops when the breaks do not improve the supervised target score, when there are tooFew examples to break, or when the tree depth is too much.
- Write a list printer that recurses down the tree and prints details about each node, indented by its level in tree.
Test: run your decision tree learner on auto.csv. Using dom and tooFew is 10, the auto.csv divides into something like this:
Source Files
All source files are present in ./src/ directory
Sdtree.py - Class for Regression Tree Learner
Setup
Code has been tested on Python 2.7.12
If uses files from previous homeworks (1, 2, 3)
Usage
To run the code,
python src/Sdtree.py data/auto.csv
Replace the path of the test file as requried
It uses some source files from HW1, HW2 and HW3. Those paths have been included wherever required
To ensure that it always runs, please run it from this (/HW4/) directory (using the same command above)
Config
The parameters: The maxDepth of the tree and min elements in the leaf nodes, are defined in ../HW1/Config.py
They have been set to maxDepth = 10 and min = 2. They can be changed as required in that file.
References
More details about the instructions can be found at Homeworks