Random Forest
Hundreds of deliberately mediocre trees, averaged — the strongest default in machine learning, and almost impossible to misuse.
Reviewed July 11, 2026Stable
When not to use it
- When you need to explain the decision. Five hundred trees is not an explanation, and post-hoc attribution is a story about the model, not the model.
- On images, text, or audio. These need learned representations. Trees operate on features you already have.
- On very large data where training time matters. Boosting is more efficient per unit of accuracy.
- On very wide, sparse data. Linear models handle text-like features better.
Reach for something else instead
- Gradient boosting — a few percent better, a day of tuning, less forgiving.
- Single decision tree — when the model must be readable.
- Logistic regression — interpretable, and competitive more often than people expect.
- Neural networks — for anything perceptual, or very large data with structure to learn.
A random forest votes across many diverse trees.
Instead of one decision tree (unstable, prone to overfitting), a random forest trains many trees, each on a random slice of data and features, then averages their votes. The diversity is the point: individual trees make different mistakes that cancel out, so the ensemble is far more robust than any single tree — with almost no tuning.
Further reading
- Breiman (2001), Random Forests — the paper; still clear, still worth reading directly.
- Grinsztajn et al. (2022), Why do tree-based models still outperform deep learning on tabular data? — the structural explanation, not just the observation.
- Fernández-Delgado et al. (2014), Do we Need Hundreds of Classifiers to Solve Real World Classification Problems? — 179 classifiers, 121 datasets; random forests came out on top overall.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Tuning n_estimators. More trees never hurt accuracy; you're tuning your patience.
- Turning off feature subsampling. It's the mechanism that decorrelates the trees — without it the forest is just slower.
- Ignoring OOB error and building a separate validation set you didn't need.
- Using default impurity importance to explain the model. It's biased toward high-cardinality features. Use permutation importance.
- Reaching for deep learning on tabular data because it's modern. The evidence says otherwise and the reasons are structural.
At a glance
FieldMachine Learning
Ideaaverage many decorrelated trees
Two randomness sourcesbagging + feature subsampling
Ceilingtree correlation, not tree count
Free evaluationout-of-bag error
Reputationhardest model to misuse
DifficultyBeginner
Often compared with
Random forest vs. gradient boosting — parallel and forgiving vs. sequential and sharper. The forest is the better default; boosting is the better result if you'll do the work.
Where this sits
A destination. 3 concepts lead here, and nothing in the corpus depends on it.
3Levelsteps in
3Needs firstconcepts
0Opens upnothing further
1Areastays here
Learn these firstDecision Tree
Computed from the prerequisite graph, not assigned. How this works