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.

Ripasso sulla regressione

Quale di questi è un problema di regressione?

Funzioni obiettivo (loss) e base learner

Alberi decisionali come base learners

Apprenditori lineari di base

Valutare la qualità del modello

Regolarizzazione e base learner in XGBoost

Usare la regolarizzazione in XGBoost

Visualizzare singoli alberi XGBoost

Visualizzare l'importanza delle feature: quali sono le più rilevanti nel mio insieme di dati

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

Conosci le basi del supervised learning e vuoi usare modelli all’avanguardia su insiemi di dati reali? Il gradient boosting è oggi una delle tecniche più popolari per modellare in modo efficiente insiemi di dati tabellari di qualsiasi dimensione. XGBoost è un’implementazione di gradient boosting molto veloce e scalabile: i modelli basati su XGBoost vincono regolarmente competizioni online di data science e sono usati su larga scala in vari settori. In questo corso imparerai a usare questa potente libreria insieme a pandas e scikit-learn per costruire e ottimizzare modelli di supervised learning. Lavorerai con insiemi di dati reali per risolvere problemi di classificazione e regressione.

Supervised Learning with scikit-learn

Esplora i fondamenti del gradient boosting, concentrandoti su XGBoost per la regressione e ottimizzazione.

Extreme Gradient Boosting con XGBoost

Impara le basi del gradient boosting e crea modelli di machine learning all'avanguardia usando XGBoost per risolvere problemi di classificazione e regressione.

Funzioni obiettivo (loss) e base learner

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