What is a Random Forest?
A Random Forest is an ensemble learning method that builds multiple decision trees and merges them together to get a more accurate and stable prediction.
Why use a Random Forest?
- Reduces Overfitting: By averaging multiple trees, it reduces the risk of overfitting.
- Handles Missing Values: It can handle missing values and maintains accuracy.
- Feature Importance: It provides an easy way to measure the relative importance of features.
Python Code Example
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
# Create dataset
X, y = make_classification(n_samples=1000, n_features=10)
# Initialize model
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X, y)