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.

Modelini neden ayarlamalısın?

Modelini ayarlamak ne zaman kötü bir fikirdir?

Boosting turu sayısını ayarlama

early_stopping kullanarak otomatik boosting turu seçimi

XGBoost'un hiperparametrelerine genel bakış

Eta'yı ayarlama

max_depth ayarlama

colsample_bytree ayarlama

Grid search ve random search'e genel bakış

XGBoost ile grid search

XGBoost ile rastgele arama

Izgara araması ve rastgele aramanın sınırları

Grid search ve random search'ü ne zaman kullanmalısın?

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

Denetimli öğrenmenin temellerini biliyor ve gerçek dünya veri kümelerinde son teknoloji modeller kullanmak mı istiyorsun? Gradient boosting, her ölçekteki tabular veri kümelerini verimli bir şekilde modellemek için günümüzde en popüler tekniklerden biri. XGBoost, gradient boosting’in çok hızlı ve ölçeklenebilir bir uygulamasıdır; XGBoost kullanan modeller çevrimiçi veri bilimi yarışmalarını düzenli olarak kazanır ve farklı sektörlerde geniş ölçekte kullanılır. Bu kursta, bu güçlü kütüphaneyi pandas ve scikit-learn ile birlikte kullanarak denetimli öğrenme modelleri kurmayı ve ayarlamayı öğreneceksin. Sınıflandırma ve regresyon problemlerini çözmek için gerçek dünya veri kümeleriyle çalışacaksın.

Supervised Learning with scikit-learn

XGBoost ile regresyon, boru hatlarında kullanımı ve model ayarlamayı öğrenin.

XGBoost'un hiperparametrelerine genel bakış

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