DataCamp
FreeMachine Learning with Tree-Based Models in Python
4.8(742)117k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This intermediate Python course explores decision trees and ensemble learning techniques using scikit-learn. It explains how ensembling methods leverage tree flexibility while controlling noise and overfitting for classification and regression tasks.
Who It's For
Python developers and data practitioners who want to master tree-based models, decision tree algorithms, and ensemble methods for competitive machine learning.
Key Takeaways
- Decision Trees: Train supervised classification and regression trees in Python using scikit-learn.
- Ensemble Methods: Combine multiple decision trees using ensembling to reduce variance and capture complex non-linear relationships.
- Hyperparameter Tuning: Learn how to tune key hyperparameters to maximize tree model performance on real-world datasets.
About this course
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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