Loading…
Glossary term
An ensemble of many decision trees whose predictions are combined.
A random forest builds many decision trees, each on a random sample of the data and a random subset of features, and combines their votes. Averaging many varied trees cancels out individual errors. It is robust and often works well with little tuning.
A random forest
from sklearn.ensemble import RandomForestClassifier
forest = RandomForestClassifier(n_estimators=200).fit(X_train, y_train)It is a dependable baseline for tabular data.