25 lines
1.6 KiB
Rust
25 lines
1.6 KiB
Rust
//! # Ensemble Methods
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//!
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//! Combining predictions of several base estimators is a general-purpose procedure for reducing the variance of a statistical learning method.
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//! When combined with bagging, ensemble models achive superior performance to individual estimators.
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//!
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//! The main idea behind bagging (or bootstrap aggregation) is to fit the same base model to a big number of random subsets of the original training
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//! set and then aggregate their individual predictions to form a final prediction. In classification setting the overall prediction is the most commonly
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//! occurring majority class among the individual predictions.
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//!
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//! In `smartcore` you will find implementation of RandomForest - a popular averaging algorithms based on randomized [decision trees](../tree/index.html).
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//! Random forests provide an improvement over bagged trees by way of a small tweak that decorrelates the trees. As in bagging, we build a number of
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//! decision trees on bootstrapped training samples. But when building these decision trees, each time a split in a tree is considered,
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//! a random sample of _m_ predictors is chosen as split candidates from the full set of _p_ predictors.
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//!
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//! ## References:
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//!
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//! * ["An Introduction to Statistical Learning", James G., Witten D., Hastie T., Tibshirani R., 8.2 Bagging, Random Forests, Boosting](http://faculty.marshall.usc.edu/gareth-james/ISL/)
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mod base_forest_regressor;
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pub mod extra_trees_regressor;
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/// Random forest classifier
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pub mod random_forest_classifier;
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/// Random forest regressor
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pub mod random_forest_regressor;
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