Random Forest
In plain terms
An ensemble of decision trees that vote on predictions.
Going deeper
Random Forest builds many decision trees on random data and feature subsets, averaging their outputs for accurate, robust predictions. Diversity reduces overfitting.
Under the hood
Bagging plus feature randomness lowers variance; handles non-linearity and gives feature importance.
Examples
Classification, regression, tabular data, feature importance, scikit-learn.
Think of it like...
Like polling a diverse crowd of experts and going with the majority view.