Random Forest
A powerful ensemble learning algorithm that builds a multitude of independent decision trees during training and merges their predictions to output a result.
Think of It Like This
Like asking fifty different experts for their opinion on a complex problem and going with the most popular consensus answer to ensure accuracy.
To prevent the trees from becoming overly correlated, Random Forest uses 'bagging' (training each tree on a random subset of data) and random feature selection at every split. This dramatically reduces the variance and overfitting problems associated with single decision trees. It remains a gold-standard algorithm for tabular data analysis.