🌲 🌲 🌲 🌲 🌲 Decision Tree Classifier 🌲 🌲 🌲 🌲 🌲

Rules:

1. Dataset must have the last column be the target column.
2. Specify this target column in tree.py's main function as "header = []" and enter the column names in the list
3. It converts any datapoint into a string because at this moment, the actual numbers are not relevant nor necessary.

How to use:

1. clone repo
2. install python
3. in tree.py's main -> specify file for training dataset, testing data, and headers
4. include build_tree for the tree, print if needed.
5. include test function for testing or just run classify on the new row

Future Proof

This tree will work on any pretty much dataset. Add as many feature columns as needed - the gini impurity and information gain will be calculated recursively on all of them.

Algorithm

Calculates Gini Impurity for all viable partitions
Based on Information Gain -> decides on order of Question

• We may use different data in the future. The Data Generation file allows us to create a large dataset by just inputting values as ranges. The generation file will then create a dataset by selecting a random value from each range as many times as you like!!! A Decision Tree is the only way to do this well

Initial very basic testing:

Current seedpack data (UserToPack.csv) configuration:

We have been given every possibility and they are represented by the range values. Key points:
From the data se can see that users are placed into a range for example '2-3', we never see plants recommended as either only 2 or only for 3 - they are not unique values in the dataset. Data also never overlaps any of the ranges. Thus we can maintain this range.