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Python에서의 앙상블 기법

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업데이트됨 2025. 10.

Python에서 bagging, boosting, stacking 등 앙상블 기법으로 고급·효과적인 머신러닝 모델을 구축하는 방법을 학습합니다.

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PythonMachine Learning

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15 동영상

52 연습 문제

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강의 설명

앙상블 학습의 멋진 세계로 뛰어들어 Machine Learning 여정을 이어가 보세요! 앙상블은 여러 개의 개별 알고리즘을 결합해 성능을 높이고, 다양한 산업 분야에서 대규모 복잡한 문제를 해결하는 흥미로운 Machine Learning 기법입니다. 온라인 Machine Learning 대회에서도 앙상블 기법이 자주 우승하곤 해요! 이 강의에서는 bagging, boosting, stacking 같은 고급 앙상블 기법을 자세히 다룹니다. scikit-learn, XGBoost, CatBoost, mlxtend와 같은 최신 Python Machine Learning 라이브러리를 사용해 실제 데이터셋에 적용해 볼 거예요.

선수 조건

Linear Classifiers in Python Machine Learning with Tree-Based Models in Python

1

Combining Multiple Models

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

Choosing the best model

Assembling your first ensemble

Evaluating your ensemble

Journey to Westeros

Predicting GoT deaths

Soft vs. hard voting

2

Bagging

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

3

Boosting

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

4

Stacking

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

Python에서의 앙상블 기법

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