Random Forest

Random Forest is a supervised learning algorithm used for both classification and regression tasks. It is an ensemble learning method that combines multiple decision trees to improve the accuracy and robustness of the predictions.

The algorithm works by creating a forest of decision trees, where each tree is trained on a randomly selected subset of the data and a random subset of the features. The trees in the forest vote to determine the final prediction, with the majority vote being the predicted class or value.

Random Forest has several advantages over single decision trees, including:

- Improved accuracy: The combination of multiple trees reduces the risk of overfitting and improves the accuracy of the predictions.
- Robustness: Random Forest is less sensitive to noise and outliers in the data than single decision trees.
- Feature importance: Random Forest can provide insight into the most important features for the prediction, which can be useful for feature selection and understanding the underlying relationships in the data.

Random Forest is widely used in various applications, including image classification, text classification, and fraud detection.