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In this course, you will :
- Introduce you to the underlying concept of XGBoost—boosted learners. Once you've figured out how XGBoost works, you'll use it to solve a common classification problem in industry: predicting whether a customer will cease to be a customer at some point in the future.
- Following a review of supervised regression, you will apply XGBoost to the regression task of predicting house prices in Ames, Iowa.
- Learn about the two types of base learners that XGboost can use as weak learners, as well as how to assess the quality of your regression models.
- how to improve the performance of your XGBoost models
- Learn about the various parameters that can be adjusted to alter the behaviour of XGBoost, as well as how to tune them efficiently to supercharge the performance of your models.
- Learn how to efficiently tune the most important XGBoost hyperparameters within a pipeline, as well as an introduction to some more advanced preprocessing techniques.
Syllabus :
- Classification with XGBoost
- Regression with XGBoost
- Fine-tuning your XGBoost model
- Using XGBoost in pipelines
