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Machine Learning with scikit-learn Quick Start Guide | 6. Classification and Regression with Trees

289 views · Published 29 October 2018 · 2:48 · Indexed 2 October 2026

Channel: Packt · 2018 · Science & Technology

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Machine Learning with scikit-learn Quick Start Guide is available from: 

Packt.com: http://bit.ly/2FnS7km
Amazon: https://amzn.to/2FrjJ8k

This is the “Code in Action” video for chapter 6 of Machine Learning with scikit-learn Quick Start Guide by Kevin Jolly, published by Packt. It includes the following topics:

00:17- Implementing the decision tree classifier in scikit-learn
00:26- Hyperparameter tuning for the decision tree
00:38- Visualizing the decision tree
00:47- Implementing the random forest classifier in scikit-learn
00:52- Hyperparameter tuning for random forest algorithms
01:21- Implementing the AdaBoost classifier in scikit-learn
01:33- Hyperparameter tuning for the AdaBoost classifier
02:00- Implementing the decision tree regressor in scikit-learn
02:07- Visualizing the decision tree regressor
02:11- Implementing the random forest regressor in scikit-learn
02:18- Implementing the gradient boosted tree in scikit-learn
02:29- Implementing the voting classifier in scikit-learn

Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize and evaluate all the important machine learning algorithms that scikit-learn provides.

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Video created by Kevin Jolly

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