What is Random Forest Algorithm?
The random forest algorithm is a powerful supervised machine learning technique used for both classification and regression tasks. It is used to find patterns in data (classification) and predicting outcomes (regression). During training, the algorithm constructs numerous decision trees, each built on a unique subset of the training data. These individual trees then vote on the final prediction, leading to a robust and accurate outcome.
In a random forest, many decision trees are made during training. Each tree is created separately using a random part of the training data. When making predictions, each tree in the forest makes its own prediction. Finally, the overall prediction is decided by combining these individual predictions. Random Forest is recommended when dealing with diverse datasets, especially when you prioritize a balance between model interpretability and performance. Its ability to avoid overfitting and work well with high-dimensional data makes it a suitable choice in a wide range of applications, including regression and classification tasks.
Random Forest vs Support Vector Machine vs Neural Network
Machine learning boasts diverse algorithms, each with its strengths and weaknesses. Three prominent are – Random Forest, Support Vector Machines (SVMs), and Neural Networks – stand out for their versatility and effectiveness. But when do you we choose one over the others? In this article, we’ll delve into the key differences between these three algorithms.
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