Do you struggle to determine which of the models you built is the best for your problem? You should give up on that, and use them all instead! In this chapter, you'll learn how to combine multiple models into one using "Voting" and "Averaging". You'll use these to predict the ratings of apps on the Google Play Store, whether or not a Pokémon is legendary, and which characters are going to die in Game of Thrones!

Introduction to ensemble methods

Exploring Google apps data

Predicting the rating of an app

Voting

Choosing the best model

Assembling your first ensemble

Evaluating your ensemble

Averaging

Journey to Westeros

Predicting GoT deaths

Soft vs. hard voting

Combining Multiple Models

Bagging is the ensemble method behind powerful machine learning algorithms such as random forests. In this chapter you'll learn the theory behind this technique and build your own bagging models using scikit-learn.

The strength of “weak” models

Restricted and unrestricted decision trees

"Weak" decision tree

Bootstrap aggregating

Training with bootstrapping

A first attempt at bagging

BaggingClassifier: nuts and bolts

Bagging: the scikit-learn way

Checking the out-of-bag score

Bagging parameters: tips and tricks

Exploring the UCI SECOM data

A more complex bagging model

Tuning bagging hyperparameters

Bagging

Boosting is class of ensemble learning algorithms that includes award-winning models such as AdaBoost. In this chapter, you'll learn about this award-winning model, and use it to predict the revenue of award-winning movies! You'll also learn about gradient boosting algorithms such as CatBoost and XGBoost.

The effectiveness of gradual learning

Introducing the movie database

Exploring movie features

Predicting movie revenue

Boosting for predicted revenue

Adaptive boosting: award winning model

Your first AdaBoost model

Tree-based AdaBoost regression

Making the most of AdaBoost

Gradient boosting

Revisiting Google app reviews

Sentiment analysis with GBM

Gradient boosting flavors

Movie revenue prediction with CatBoost

Boosting contest: Light vs Extreme

Boosting

Get ready to see how things stack up! In this final chapter you'll learn about the stacking ensemble method. You'll learn how to implement it using scikit-learn as well as with the mlxtend library! You'll apply stacking to predict the edibility of North American mushrooms, and revisit the ratings of Google apps with this more advanced approach.

The intuition behind stacking

Exploring the mushroom dataset

Predicting mushroom edibility

K-nearest neighbors for mushrooms

Build your first stacked ensemble

Applying stacking to predict app ratings

Building the stacking classifier

Stacked predictions for app ratings

Let's mlxtend it!

A first attempt with mlxtend

Back to regression with stacking

Mushrooms: a matter of life or death

Ensembling it all together

Stacking

App ratings

App reviews

Game of Thrones

Pokémon

SECOM (Semiconductor Manufacturing)

TMDb (The Movie Database)

Continue your machine learning journey by diving into the wonderful world of ensemble learning methods! These are an exciting class of machine learning techniques that combine multiple individual algorithms to boost performance and solve complex problems at scale across different industries. Ensemble techniques regularly win online machine learning competitions as well!
In this course, you’ll learn all about these advanced ensemble techniques, such as bagging, boosting, and stacking. You’ll apply them to real-world datasets using cutting edge Python machine learning libraries such as scikit-learn, XGBoost, CatBoost, and mlxtend.

Linear Classifiers in Python

Machine Learning with Tree-Based Models in Python

Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting and stacking.

Ensemble Methods in Python

Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.

Aprendizaje automático supervisado en Python

Data Scientist, Appodeal

Surveys

Prerequisite knowledge

Technologies

Learners, ensemble!

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Combining Multiple Models - Introduction to ensemble methods

Introduction to ensemble methods

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