Machine Learning with Tree-Based Models in Python
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Course overview
- Provider
- Datacamp
- Course type
- Free trial availiable
- Deadline
- Flexible
- Duration
- 5 hours
- Certificate
- Available on completion
- Course author
- Elie Kawerk
Description
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Decision trees are supervised learning models used for problems involving classification and regression. Tree models present a high flexibility that comes at a price: on one hand, trees are able to capture complex non-linear relationships; on the other hand, they are prone to memorizing the noise present in a dataset. By aggregating the predictions of trees that are trained differently, ensemble methods take advantage of the flexibility of trees while reducing their tendency to memorize noise. Ensemble methods are used across a variety of fields and have a proven track record of winning many machine learning competitions.
In this course, you'll learn how to use Python to train decision trees and tree-based models with the user-friendly scikit-learn machine learning library. You'll understand the advantages and shortcomings of trees and demonstrate how ensembling can alleviate these shortcomings, all while practicing on real-world datasets. Finally, you'll also understand how to tune the most influential hyperparameters in order to get the most out of your models.
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