Decision Trees

Let's think about it this way for a second

A Decision Tree is a tree structure that splits data using a series of questions (e.g. 'age > 30?') until it reaches a leaf node and makes a decision — because it's human-readable (unlike, say, Linear Regression's coefficients), you can clearly explain 'why this prediction was made'. If you don't limit the tree's depth (the number of question layers), it can grow deep enough to 'memorize' the training data (overfitting risk) — you limit this with the `max_depth` parameter.

Let's connect it to a real scenario

Writing `from sklearn.tree import DecisionTreeClassifier; model = DecisionTreeClassifier(max_depth=5); model.fit(X_train, y_train)` limits the tree depth to 5 layers, keeping overfitting in check — running `from sklearn.tree import plot_tree; plot_tree(model)` lets you view the tree structure as a visual diagram, clearly showing which feature is split at which threshold.

• Decision Tree = a question-chain structure, human-readable/interpretable
• max_depth = limits the tree's depth (to keep overfitting in check)
• Works for both Regression and Classification (DecisionTreeRegressor/DecisionTreeClassifier)

Let's look at it together

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

model = DecisionTreeClassifier(max_depth=5, random_state=42)
model.fit(X_train, y_train)

print(f"Accuracy: {model.score(X_test, y_test):.2f}")

# Visualize the tree structure
plot_tree(model, feature_names=X.columns, filled=True)
plt.show()

Accuracy: 0.84