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.

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

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.

Mengapa perlu menyetel model Anda?

Kapan menyetel model Anda bukan ide yang baik?

Menyetel jumlah boosting rounds

Pemilihan jumlah boosting secara otomatis dengan early_stopping

Gambaran umum hyperparameter XGBoost

Menyetel eta

Menyetel max_depth

Men-tuning colsample_bytree

Tinjauan grid search dan random search

Grid search dengan XGBoost

Pencarian acak dengan XGBoost

Batasan grid search dan random search

Kapan sebaiknya Anda menggunakan grid search dan 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.

Mengapa perlu menyetel model Anda?

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