Random forest is a machine-learning (ML) method applied to classification and regression problems. As an ensemble learning approach, it combines the outcomes of multiple models to generate a single, more accurate prediction.
A random forest consists of numerous decision trees, each trained on a randomly selected subset of the data. The final prediction is determined by averaging the predictions of all these decision trees. Each decision tree makes its prediction based on the features of the data it has been trained on.
One of the significant advantages of random forest is its ability to manage a large number of features while being highly resistant to overfitting. Additionally, it can effectively deal with missing values and outliers. Moreover, it can offer an evaluation of the importance of each feature, which helps in understanding the relative significance of different variables in the dataset.
Random forest is a potent and extensively used ML algorithm. It finds applications in diverse fields such as image classification, recommendation systems, and fraud detection. It serves as an efficient tool for both classification and regression tasks, excelling at handling numerous features and avoiding overfitting.