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.

L'efficacia dell'apprendimento graduale

Presentazione del database dei film

Esplorare le caratteristiche dei film

Prevedere gli incassi dei film

Boosting per il fatturato previsto

Adaptive boosting: un modello premiato

Il tuo primo modello AdaBoost

Regressione AdaBoost basata su alberi

Sfruttare al massimo AdaBoost

Gradient boosting

Rivedere le recensioni delle app di Google

Analisi del sentiment con GBM

Varianti del gradient boosting

Previsione degli incassi dei film con CatBoost

Sfida boosting: 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)

Continua il tuo percorso nel machine learning esplorando l’affascinante mondo dei metodi di ensemble! Si tratta di una classe entusiasmante di tecniche di machine learning che combinano più algoritmi per migliorare le prestazioni e risolvere problemi complessi su larga scala in diversi settori. Le tecniche di ensemble vincono spesso anche le competizioni online di machine learning!
In questo corso imparerai tutto su queste tecniche avanzate di ensemble, come bagging, boosting e stacking. Le applicherai a insiemi di dati reali usando librerie Python all’avanguardia per il machine learning, come scikit-learn, XGBoost, CatBoost e mlxtend.

Linear Classifiers in Python

Machine Learning with Tree-Based Models in Python

Impara a creare modelli avanzati di machine learning in Python con tecniche ensemble come bagging e boosting.

Metodi Ensemble in Python

Impara a creare modelli di machine learning avanzati ed efficaci in Python usando tecniche di ensemble come bagging, boosting e stacking.

Adaptive boosting: un modello premiato

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