Tree-based decision making.

What are Decision Trees?

Like a flowchart that makes decisions!

Each branch asks a question, leading to final decision.

Example Decision Process

**Goal**: Should I go to the beach in Miami?

``` Is it sunny? ├─ Yes → Is temperature > 70°F? │ ├─ Yes → Go to beach! │ └─ No → Stay home └─ No → Stay home ```

How It Works

1. Find best question to split data 2. Create branches 3. Repeat for each branch 4. Stop when data is pure or max depth reached

Python Example

```python from sklearn.tree import DecisionTreeClassifier

Weather data: [temperature, sunny] X = [[85, 1], [70, 1], [60, 0], [50, 0]] y = ['beach', 'beach', 'home', 'home']

model = DecisionTreeClassifier(max_depth=2) model.fit(X, y)

Predict prediction = model.predict([[75, 1]]) print(prediction) # 'beach' ```

Advantages

- Easy to understand - No data scaling needed - Works with numbers and categories - Visual representation

Disadvantages

- Can overfit easily - Unstable (small data change = different tree) - Not great for very complex data

Hyperparameters

**max_depth**: Maximum tree depth **min_samples_split**: Minimum samples to split **min_samples_leaf**: Minimum samples in leaf

Remember

- Very intuitive algorithm - Prone to overfitting - Use Random Forest for better results