The Random Forest algorithm is an ensemble technique that constructs numerous decision trees and merges their predictions to enhance accuracy in data classification and regression. It utilizes bootstrap sampling and random feature selection to diversify each tree, reducing overfitting. This method not only improves predictive performance but also ranks feature importance, providing significant insights for data analysis. Its ability to process large datasets and manage missing values makes it accessible for both novice and experienced data analysts, cementing its reputation as a go-to solution in machine learning.
The Random Forest algorithm combines multiple trees to create a robust and accurate prediction model.
The Random Forest classifier combines multiple decision trees using ensemble learning principles, automatically determines feature importance, handles classification and regression tasks effectively, and seamlessly manages missing values and outliers.
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