decision-tree-python
Decision tree implementation from scratch in python.
Virtual environment
Creating:
conda create --name decision-tree python=3.8
Activating:
conda activate decision-tree
Installing packages:
pip install -r requirements.txt
Registrating the environment in a notebook
ipython kernel install --name "decision-tree" --user
Usage
IMPORTANT: only use numeric features for the X matrices.
Feel free to create a pull request with the additional implementation.
Classification tree
# Reading data
d = pd.read_csv("data/classification/train.csv")[['Age', 'Fare', 'Survived']].dropna()
# Constructing the X and Y matrices
X = d[['Age', 'Fare']]
Y = d['Survived'].values.tolist()
# Initiating the Node
root = Node(Y, X, max_depth=3, min_samples_split=100)
# Getting teh best split
root.grow_tree()
# Printing the tree information
root.print_tree()
Regression tree
# Reading data
d = pd.read_csv("data/regression/auto-mpg.csv")
# Subsetting
d = d[d['horsepower']!='?']
# Constructing the X and Y matrices
features = ['horsepower', 'weight', 'acceleration']
for ft in features:
d[ft] = pd.to_numeric(d[ft])
X = d[features]
Y = d['mpg'].values.tolist()
# Initiating the Node
root = NodeRegression(Y, X, max_depth=3, min_samples_split=3)
# Growing the tree
root.grow_tree()
# Printing tree
root.print_tree()