Responsible Machine Learning with Python Examples of techniques for training interpretable machine learning (ML) models, explaining ML models, and debugging ML models for accuracy, discrimination, and ...

The oblique decision tree is a popular choice in the machine learning domain for improving the performance of traditional decision tree algorithms. In contrast to the traditional decision tree, which ...

This GitHub repository contains the code examples of the 1st Edition of Python Machine Learning book. If you are looking for the code examples of the 2nd Edition, please refer to this repository ...

After earlier explaining how to compute disorder and split data in his exploration of machine learning decision tree classifiers, resident data scientist Dr. James McCaffrey of Microsoft Research now ...

A decision tree is a machine learning technique that can be used for binary classification or multi-class classification. A multi-class classification problem is one where the goal is to predict the ...

Key Takeaways The transition requires upskilling in Python, statistics, and machine learning.Practical experience with ...

Low-code machine learning is gaining popularity with tools like PyCaret, H2O.ai and DataRobot, allowing data scientists to run pre-canned patterns for feature engineering, data cleansing, model ...

Discover five powerful Python libraries that enable data scientists to interpret and explain machine learning models effectively.