Machine Learning 101 P6: Random Forest Regression with Python

Introduction

Radom Forest is one of the most popular algorithm used in regression problem thanks to its simplicity and high accuracy. Here are the full list of benefits using this model:

• Non-linear Relationships: Random Forest handles complex, non-linear relationships between input features and the target variable better than linear regression.
• Robustness to Overfitting: Unlike individual decision trees, random forests reduce overfitting due to ensemble averaging, making them more reliable for noisy data. (will be elaborated further in later section)
• High-Dimensional Data: The algorithm performs well when there are many features, as it randomly selects subsets of features for splitting.
• Feature Importance Analysis: If understanding the influence of different features on predictions is essential, random forests provide useful insights.
• Missing Values and Scaling: Random Forest is robust to missing data and does not require extensive feature scaling or normalization.
• Small to Medium-Sized Datasets: It is particularly effective for datasets where deep learning might overfit due to limited data.

Source: https://www.keboola.com/blog/random-forest-regression