addyg / decision-tree-from-scratch

Python code for a decision tree built from scratch (w/o sckit-learn). Works for continuous/numerical variables.

Decision-Tree-From-Scratch

Python code for a decision tree built from scratch (w/o sckit-learn). Works for continuous/numerical variables.

Task:

1. construct a decision tree classifier from the given training data
2. use the learned tree classifier to classify the unlabeled test data, and
3. output the predictions of your classifier on the test data into a file named blackbox1*_predictions.csv

The format of *_train.csvlooks like: x1, x2, y Where x1 andx2are the attribute values and yis the label, and _test.csvare unlabeled. Your output blackbox1_predictions.csvwill look like 1 0 0 É. (A single column indicates the predicted labels for each unlabeled sample in the input test file)