A random forest is a machine learning algorithm that uses multiple decision trees to make predictions. It's a popular and flexible algorithm that can be used for classification and regression tasks.?
A Random Forest is a collection of decision trees that work together to make predictions.
They are widely used for classification and regression task.
- It is a type of classifier that uses many decision trees to make predictions.
- It takes different random parts of the dataset to train each tree and then it combines the results by averaging them. This approach helps improve the accuracy of predictions.?Random Forest is based on ensemble learning.
- Key Features of Random Forest
- Handles Missing Data: Automatically handles missing values during training, eliminating the need for manual imputation.
- Algorithm ranks features based on their importance in making predictions offering valuable insights for feature selection and interpretability.
- Scales Well with Large and Complex Data without significant performance degradation.
- Algorithm is versatile and can be applied to both classification tasks (e.g., predicting categories) and regression tasks (e.g., predicting continuous values).
