Random Forest

What it does

Averages many decision trees to improve prediction, with configurable size and feature sampling.

Use it as a strong general-purpose predictor.

Data you need

• Target
• Numeric features

Supported data: Numeric, Categorical. Level: Intermediate.

Formula and method

ŷ = (1/B) Σ Tᵦ(x)

Each tree is grown on a bootstrap sample, with a random subset of features considered at each split. Votes or averages across trees reduce variance. Permutation importance is measured on the test set.

How to read the result

Use permutation importance to see which features matter.

Worked example

Illustrative run on Fisher iris measurements. These figures are the actual output of this model on that example, and they say nothing about your own data.

• Test accuracy = 0.9211 against a majority-class baseline of 0.3421; F1 = 0.923.
• The model is better than always predicting the most common class on this test set.

Limitations

• Harder to interpret than a single tree

Numerical validation

Validated 16 of 16 automated checks passed, including numerical results, invalid input, missing values, small samples, constant variables, charts and the interpretation text.

Computed with python 3.13.2, numpy 2.2.5, scipy 1.14.1, pandas 2.3.3, statsmodels 0.14.4, scikit-learn 1.7.0, in your browser. A passing check confirms the listed comparison only. It does not make any result suitable for your data, and it is not a claim of equivalence with other statistical software.

Questions

Does feature importance show causation?

No. It shows what helped the model predict.