# Main take-away

## Wrap-up

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So in this module, we discussed ensemble learners which are a type of
learner that combines simpler learners together. We saw two strategies:

- one based on bootstrap samples allowing learners to be fit in a parallel
  manner;
- the other called boosting which fit learners sequentially.

From these two families, we mainly focused on giving intuitions regarding the
internal machinery of the random forest and gradient-boosting models which
are state-of-the-art methods.

## To go further

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You can refer to the following scikit-learn examples which are related to
the concepts approached in this module:

- [Early-stopping in gradient-boosting](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_early_stopping.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-early-stopping-py)
- [Combining predictors using stacking](https://scikit-learn.org/stable/auto_examples/ensemble/plot_stack_predictors.html#sphx-glr-auto-examples-ensemble-plot-stack-predictors-py)