[Feature] Add Random Forest Classifier to linfa-trees

[maxprogrammer007]

📝 Description

I would like to contribute a new module to the linfa-trees crate that implements the Random Forest algorithm for classification tasks. This will expand linfa-trees from single decision trees into ensemble learning, aligning closely with scikit-learn's functionality in Python.

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🚀 Motivation

Random Forests are a powerful ensemble learning method used widely in classification tasks. They provide:

• Robustness to overfitting

• Better generalization than single trees

• Feature importance estimates

Currently, linfa-trees provides support for single decision trees. By adding Random Forests, we unlock ensemble learning for the Rust ML ecosystem.

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📐 Proposed Design

🔹 New Module

A new file will be added:

bash
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linfa-trees/src/decision_trees/random_forest.rs

This will include:

• RandomForestClassifier<F: Float>

• RandomForestParams<F> (unchecked)

• RandomForestValidParams<F> (checked)

🔹 Trait Implementations

I will implement the following traits according to linfa conventions:

• ParamGuard for parameter validation

• Fit to train the forest using bootstrapped data and random feature subsetting

• PredictInplace and Predict to perform inference via majority voting

🔹 Example

An example will be added in:

bash
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linfa-trees/examples/iris_random_forest.rs

Using the Iris dataset from linfa-datasets.

🔹 Benchmark (Optional)

If approved, I can also add a benchmark using Criterion:

bash
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linfa-trees/benches/random_forest.rs

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📁 File Integration Plan

• src/lib.rs: Re-export random_forest::*

• src/decision_trees/mod.rs: pub mod random_forest;

• README.md: Update with a section on Random Forests and example usage

• examples/iris_random_forest.rs: Demonstrates training and evaluation

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📦 API Preview

rust
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let model = RandomForest::params()
    .n_trees(100)
    .feature_subsample(0.8)
    .max_depth(Some(10))
    .fit(&dataset)?;
let predictions = model.predict(&dataset);

let acc = predictions.confusion_matrix(&dataset)?.accuracy();

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✅ Conformity with CONTRIBUTING.md

• Uses Float trait for f32/f64 compatibility

• Follows the Params → ValidParams validation pattern

• Implements Fit, Predict, and PredictInplace using Dataset

• Optional serde support via feature flag

• Will include unit tests and optionally benchmarks

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🙋‍♂️ Request

Please let me know if you're open to this contribution. I’d be happy to align with maintainers on:

• Feature scope (classifier first, regressor later?)

• Benchmarking standards

• Integration strategy (e.g., reuse of DecisionTree)

Looking forward to your guidance!

[relf]

Thanks for your thorough description. This looks good to me, please proceed with a PR!

[relf]

Sorry, just noticed previous art in #229. It would be great to take a look at it before jumping on a whole new implementation.

[maxprogrammer007]

@relf Sure i will look into #229 and afterwards i will prepare my PR

[maxprogrammer007]

@relf

I have done a PR, please see .. all checks have been passed and i have successfully tested the module.

PR link - #390