Random Forest is a versatile and widely used machine learning algorithm that operates as an ensemble of decision trees. It's used for both regression and classification tasks. The fundamental idea behind a Random Forest is to build multiple decision trees during training and aggregate their predictions to make more accurate and robust predictions.

Here's an overview of how Random Forest works:

1. Decision Trees: Random Forest comprises multiple decision trees, each trained on a subset of the dataset and using a random selection of features.

2. Random Subsets: During training, Random Forest randomly selects subsets of the original dataset (sampling with replacement, known as bootstrapping) to build individual decision trees. Additionally, it randomly selects a subset of features at each node of the tree, which helps in decorrelating the trees and increasing diversity among them.

3. Bagging and Aggregation: Each decision tree in the Random Forest is built independently, and predictions from individual trees are aggregated to make the final prediction. For regression tasks, the predictions from each tree are averaged, and for classification tasks, the mode (most frequent prediction) among the trees is taken as the final prediction.

4. Ensemble Learning: By combining multiple decision trees trained on different subsets of the data and features, Random Forest reduces overfitting and increases the model's generalization ability. It's robust against outliers and noise in the data and tends to provide more accurate predictions compared to individual decision trees.

Random Forests are highly flexible, perform well on a wide range of datasets, and require minimal hyperparameter tuning. They are used in various applications such as classification tasks (like spam detection, medical diagnosis, etc.), regression tasks (like predicting prices or quantities), feature importance analysis, and more. Their ability to handle large datasets, high dimensionality, and non-linear relationships makes them popular in the machine learning community.