Random Forest
Random Forest is an ensemble learning algorithm that builds many decision trees on randomly resampled data and random feature subsets, then aggregates their predictions by voting or averaging to produce a more accurate and robust model.
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Glossary Terms(7)
Ensemble Learning
Ensemble learning is a machine learning approach that combines the predictions of multiple individual models to produce a single, typically more accurate and r…
Random Forest
Random Forest is an ensemble learning algorithm that builds many decision trees on randomly resampled data and random feature subsets, then aggregates their pr…
Gradient Boosting
Gradient Boosting is an ensemble learning technique that builds a model as a sequence of weak learners, typically shallow decision trees, where each new learne…
XGBoost
XGBoost (Extreme Gradient Boosting) is an open-source, highly optimized implementation of the gradient boosting algorithm, widely used for structured/tabular d…
Decision Tree
A decision tree is a supervised learning model that predicts an outcome by learning a hierarchy of simple, interpretable if-then decision rules based on input…
Bootstrap Aggregating
Bootstrap aggregating, commonly called bagging, is an ensemble learning technique that trains multiple instances of a model on different bootstrap-sampled subs…
Bagging
Bagging, short for bootstrap aggregating, is an ensemble learning method that trains multiple copies of a model on different randomly resampled (bootstrapped)…