Random Forest Tutorial
This collection of notebooks is designed to help you learn about Random Forests, a powerful Machine Learning algorithm used in classification and regression tasks. Through a series of hands-on tutorials and examples, you'll gain a deep understanding of how Random Forests work and how to apply them to real-world problems. From the basics of training and evaluating Random Forest models to more advanced topics like hyperparameter tuning.
Train Random Forest classifier
Python implementation of Random Forest algorithm available in `scikit-learn` package is very popular. In this notebook, we will train Random Forest classifier. We will use Iris dataset, which presents mutliclass classification task.
Train Random Forest regressor
The `scikit-learn` provides implementation of [Random Forest](/glossary/random-forest/) algorithm. It can be used to predict continuous target. In this notebook, we will use Python code to train `RandomForestRegressor` to predict real estate prices.