203_Classification_Tree_Basic.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 30 09:32:54 2025

@author: kingsleylee
"""


# Classification Tree - Basic Template




# Import required Python packages

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import pandas as pd



# Import sample data

my_df = pd.read_csv("data/sample_data_classification.csv")



# Split data into input and output objects

X = my_df.drop(["output"], axis = 1)
y = my_df["output"]



# Split data into training and text sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42, stratify = y)



# Instantiate our model object

clf = DecisionTreeClassifier(random_state = 42, min_samples_leaf = 7)



# Train our model

clf.fit(X_train, y_train)



# Assess model accuracy

y_pred = clf.predict(X_test)
accuracy_score(y_test, y_pred)



# A Demonstration of Overfitting

y_pred_training = clf.predict(X_train)
accuracy_score(y_train, y_pred_training)


# Plot our Decision Tree

import matplotlib.pyplot as plt
from sklearn.tree import plot_tree

plt.figure(figsize = (25, 15))
tree = plot_tree(clf,
                 feature_names = list(X.columns),
                 filled = True, 
                 rounded = True,
                 fontsize = 24)