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

Classification 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.

Tinjauan regresi

Manakah dari berikut ini yang merupakan masalah regresi?

Fungsi objektif (loss) dan base learner

Pohon keputusan sebagai base learner

Pemelajar dasar linear

Mengevaluasi kualitas model

Regularisasi dan pembelajar dasar di XGBoost

Menggunakan regularisasi di XGBoost

Memvisualisasikan pohon XGBoost secara individual

Memvisualisasikan kepentingan fitur: Fitur apa yang paling penting dalam himpunan data saya

Regression with XGBoost

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 eta

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?

Fine-tuning your XGBoost model

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

Using XGBoost in pipelines

Ames housing prices (preprocessed)

Ames housing prices (original)

Chronic kidney disease

Apakah Anda memahami dasar-dasar supervised learning dan ingin menggunakan model mutakhir pada himpunan data dunia nyata? Gradient boosting saat ini merupakan salah satu teknik paling populer untuk pemodelan efisien pada himpunan data tabular dari berbagai ukuran. XGBoost adalah implementasi gradient boosting yang sangat cepat dan dapat diskalakan, dengan model berbasis XGBoost yang secara rutin memenangkan kompetisi data science daring dan digunakan secara luas di berbagai industri. Dalam kursus ini, Anda akan mempelajari cara menggunakan library yang kuat ini bersama pandas dan scikit-learn untuk membangun dan menyetel model supervised learning. Anda akan bekerja dengan himpunan data dunia nyata untuk menyelesaikan masalah klasifikasi dan regresi.

Supervised Learning with scikit-learn

Jelajahi dasar-dasar gradient boosting, fokus pada Regresi dengan XGBoost, dan cara menyetel model XGBoost.

Extreme Gradient Boosting dengan XGBoost

Pelajari dasar-dasar gradient boosting dan bangun model machine learning terkini menggunakan XGBoost untuk menyelesaikan masalah klasifikasi dan regresi.

Fungsi objektif (loss) dan base learner

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