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.

Herhaling van regressie

Welke van deze is een regressieprobleem?

Objective- (loss)functies en basis learners

Beslisbomen als basisleerders

Lineaire basismodellen

Modelkwaliteit evalueren

Regularisatie en basislearners in XGBoost

Regularisatie gebruiken in XGBoost

Individuele XGBoost-bomen visualiseren

Feature-importances visualiseren: welke features zijn het belangrijkst in mijn gegevensset

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

Ken je de basis van supervised learning en wil je state-of-the-art modellen toepassen op echte datasets? Gradient boosting is momenteel een van de populairste technieken voor efficiënt modelleren van tabelvormige datasets van elke omvang. XGBoost is een zeer snelle, schaalbare implementatie van gradient boosting. Modellen met XGBoost winnen regelmatig online data science-wedstrijden en worden op grote schaal gebruikt in uiteenlopende sectoren. In deze cursus leer je hoe je deze krachtige bibliotheek samen met pandas en scikit-learn gebruikt om supervised learning-modellen te bouwen en te finetunen. Je werkt met echte datasets om classificatie- en regressieproblemen op te lossen.

Supervised Learning with scikit-learn

Verken de basis van gradient boosting, focus op XGBoost-regressie, gebruik in pipelines en modelafstemming.

Extreme Gradient Boosting met XGBoost

Leer de basis van gradient boosting en bouw toffe machine learning-modellen met XGBoost om classificatie- en regressieproblemen op te lossen.

Objective- (loss)functies en basis learners

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