# Module overview

## What you will learn

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This module will go into details regarding algorithms that are combining
several models together, also called ensemble of models. We will present two
families of such techniques: (i) based on bootstrapping and (ii) based
on boosting. We will present bagging and random forest that belong to the
former strategy and AdaBoost and gradient boosting decision tree that belong
to the later strategy. Finally, we will go into details regarding the
hyperparameters allowing to tune these models and compare them between models.

## Before getting started

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The required technical skills to carry on this module are:

- skills acquired during the "The Predictive Modeling Pipeline" module with
  basic usage of scikit-learn;
- skills acquired during the "Selecting The Best Model" module, mainly around
  the concept of underfit/overfit and the usage of cross-validation in
  scikit-learn;
- skills acquired during the modules "Linear Models" and
  "Decision Tree Models".

<!-- Point to resources to learning these skills -->

## Objectives and time schedule

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The objective in the module are the following:

- understanding the principles behind bootstrapping and boosting;
- get intuitions with specific models such as random forest
  and gradient boosting;
- identify the important hyperparameters of random forest and gradient boosting
  decision trees as well as their typical values.

<!-- Give the investment in time -->

The estimated time to go through this module is about 6 hours.