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XGBoost로 익히는 Extreme Gradient Boosting

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업데이트됨 2026. 3.

Gradient boosting의 핵심을 배우고, XGBoost로 분류·회귀 문제를 해결하는 최첨단 머신 러닝 모델을 구축하세요.

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PythonMachine Learning

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16 동영상

49 연습 문제

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강의 설명

지도 학습의 기초를 알고 있고, 실제 데이터셋에 최신 모델을 적용해 보고 싶으신가요? Gradient boosting은 현재 크고 작은 표 형식(tabular) 데이터셋을 효율적으로 모델링하는 데 가장 인기 있는 기법 중 하나입니다. XGBoost는 gradient boosting을 매우 빠르고 확장 가능하게 구현한 라이브러리로, XGBoost를 활용한 모델은 온라인 데이터 사이언스 대회에서 꾸준히 우승하고 다양한 산업 현장에서 대규모로 사용되고 있어요. 이 강의에서는 pandas와 scikit-learn과 함께 이 강력한 라이브러리를 사용해 지도 학습 모델을 구축하고 튜닝하는 방법을 배웁니다. 실제 데이터셋을 다루며 분류와 회귀 문제를 해결해 볼 거예요.

선수 조건

Supervised Learning with scikit-learn

1

Classification with XGBoost

This chapter will introduce you to the fundamental idea behind XGBoost—boosted learners. Once you understand how XGBoost works, you'll apply it to solve a common classification problem found in industry: predicting whether a customer will stop being a customer at some point in the future.

Welcome to the course!

Which of these is a classification problem?

Which of these is a binary classification problem?

Introducing XGBoost

XGBoost: Fit/Predict

What is a decision tree?

Decision trees

What is Boosting?

Measuring accuracy

Measuring AUC

When should I use XGBoost?

Using XGBoost

2

Regression with XGBoost

After a brief review of supervised regression, you'll apply XGBoost to the regression task of predicting house prices in Ames, Iowa. You'll learn about the two kinds of base learners that XGboost can use as its weak learners, and review how to evaluate the quality of your regression models.

Regression review

Which of these is a regression problem?

Objective (loss) functions and base learners

Decision trees as base learners

Linear base learners

Evaluating model quality

Regularization and base learners in XGBoost

Using regularization in XGBoost

Visualizing individual XGBoost trees

Visualizing feature importances: What features are most important in my dataset

3

Fine-tuning your XGBoost model

This chapter will teach you how to make your XGBoost models as performant as possible. You'll learn about the variety of parameters that can be adjusted to alter the behavior of XGBoost and how to tune them efficiently so that you can supercharge the performance of your models.

Why tune your model?

When is tuning your model a bad idea?

Tuning the number of boosting rounds

Automated boosting round selection using early_stopping

Overview of XGBoost's hyperparameters

Tuning max_depth

Tuning colsample_bytree

Review of grid search and random search

Grid search with XGBoost

Random search with XGBoost

Limits of grid search and random search

When should you use grid search and random search?

4

Using XGBoost in pipelines

Take your XGBoost skills to the next level by incorporating your models into two end-to-end machine learning pipelines. You'll learn how to tune the most important XGBoost hyperparameters efficiently within a pipeline, and get an introduction to some more advanced preprocessing techniques.

Review of pipelines using sklearn

Exploratory data analysis

Encoding categorical columns I: LabelEncoder

Encoding categorical columns II: OneHotEncoder

Encoding categorical columns III: DictVectorizer

Preprocessing within a pipeline

Incorporating XGBoost into pipelines

Cross-validating your XGBoost model

Kidney disease case study I: Categorical Imputer

Kidney disease case study II: Feature Union

Kidney disease case study III: Full pipeline

Tuning XGBoost hyperparameters

Bringing it all together

Final Thoughts

XGBoost로 익히는 Extreme Gradient Boosting

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