Introduction
Page Summary
Decision forests are interpretable machine learning algorithms that work well with tabular data for tasks like classification, regression, and ranking.
Decision forests offer advantages such as easy configuration, native handling of various data types, robustness to noise, and fast inference/training on smaller datasets.
This course provides a comprehensive understanding of decision trees and forests, including how they make predictions, different types, performance considerations, and effective usage strategies.
The course uses YDF library code examples to demonstrate concepts, but the knowledge is transferable to other decision forest libraries.
Basic machine learning knowledge and familiarity with data preprocessing are prerequisites for this course.
Decision forests provide the following benefits:
- They areeasier to configure than neural networks. Decision forestshavefewer hyperparameters; furthermore, the hyperparameters in decisionforests providegood defaults.
- Theynatively handle numeric, categorical, and missing features. Thismeans you can write far less preprocessing code than when using a neuralnetwork, saving you time and reducing sources for error.
- They often givegood results out of the box, are robust to noisy data,and have interpretable properties.
- They infer and train on small datasets (< 1M examples)much faster thanneural networks.
Decision forests produce great results in machine learning competitions, andare heavily used in many industrial tasks.
This course introduces decision trees and decision forests.Decision forests are a family ofinterpretable machine learningalgorithms that excel with tabular data.Decision forests can perform:
Learning Objectives:- Explain decision trees and decision forests.
- Determine how decision trees and decision forests make predictions.
- Understand how different types of decision forests, such as random forests and gradient boosted trees.
- Explain when decision forests perform well, and what their limitations are.
- Develop a sense of how to use decision forests effectively.
Prerequisites
This course assumes you have completed the following courses or have equivalentknowledge:
Happy Learning!
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Last updated 2025-08-25 UTC.