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Glossary · Machine Learning

What is Random Forest?

An ensemble model that combines many decision trees trained on random samples of data and features.

In more detail

Averaging many different trees reduces overfitting. Random forests are accurate with little tuning, need no scaling and provide feature importance.

Example

RandomForestClassifier(n_estimators=300)

Learn it properlyClassification Models: Logistic Regression, Decision Trees and Random Forests13 min lesson · Machine Learning

Related terms

Cross-ValidationEvaluating a model by training and validating it several times on different splits of the data and averaging the results.Data LeakageWhen information unavailable at prediction time sneaks into training, making a model look better than it really is.Feature EngineeringCreating, transforming and selecting input variables so a model can learn patterns more easily.Linear RegressionA model that predicts a number as a weighted sum of input features — a best-fit straight line or plane.Precision and RecallTwo classification metrics: precision is how many flagged items were correct; recall is how many actual positives were caught.Train-Test SplitDividing data into a part for training a model and a held-out part for checking how it performs on unseen data.