メインコンテンツへスキップ

ホーム Python

コース

Pythonで学ぶアンサンブル手法

上級スキルレベル

更新日 2025/10

Pythonでbagging、boosting、stackingなどのアンサンブル手法を用い、高度で効果的な機械学習モデルの構築を学びます。

コースを無料で開始

PythonMachine Learning

4時間

15 ビデオ

52 演習

4,050 XP

12,854

修了証明書

何千もの企業の従業員が支持

チームのトレーニングを担当していますか？

Businessをお試しください

コース説明

Machine Learning の学習を続けて、アンサンブル学習の世界に踏み込みましょう！アンサンブルは、複数の個別アルゴリズムを組み合わせて性能を高め、さまざまな業界で大規模な課題を解決する強力な手法です。オンラインの Machine Learning コンペでも常に上位に入賞しています。このコースでは、bagging、boosting、stacking などの高度なアンサンブル手法を学びます。scikit-learn、XGBoost、CatBoost、mlxtend といった最先端の Python ライブラリを使って、実データに適用していきます。

前提条件

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

チャプターを開始

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

チャプターを開始

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

チャプターを開始

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

チャプターを開始

Pythonで学ぶアンサンブル手法

コース完了

修了証明書を取得

この修了書をLinkedInや履歴書、CVに追加しましょう
ソーシャルメディアや人事評価で共有しましょう今すぐ登録

19百万人を超える学習者と共にPythonで学ぶアンサンブル手法を始めましょう！

DataCamp for Mobileでデータスキルを磨きましょう

モバイルコースと毎日の 5 分間のコーディングチャレンジで、外出先でも進歩できます。