XGBoost and Random Forest are machine learning algorithms used for prediction and classification tasks. They learn patterns from data by combining multiple decision trees.
- Random Forest improves accuracy by averaging many independent trees.
- XGBoost builds trees sequentially, focusing on correcting previous errors.
Both are widely used for feature importance analysis and robust predictive modeling.
It was applied to the following weather dataset: https://www.kaggle.com/jsphyg/weather-dataset-rattle-package