Machine Learning with Rules using Python skope-rules Training Course

Last updated

Course Code

scoperules

Duration

14 hours (usually 2 days including breaks)

Requirements

  • Python programming experience
  • Knowledge of machine learning algorithms

Audience

  • Developers

Overview

Skope-rules is a Python machine learning module built on top of scikit-learn.

In this instructor-led, live training (onsite or remote), participants will learn how to use Python skope-rules to automatically generate rules based on existing data sets.

By the end of this training, participants will be able to:

  • Use skope-rules to extract rules from available data.
  • Apply skope-rules to carry out classification, particularly useful in supervised anomaly detection, or imbalanced classification. 
  • Generate rules for classifying new incoming data.
  • Fit rules to address real-world problems in fraud detection, predictive maintenance, intrusion detection, insurance application approvals, etc.

Format of the Course

  • Part lecture, part discussion, exercises and heavy hands-on practice in a live-lab environment.

Note

  • To request a customized training for this course, please contact us to arrange.
  • To learn more about skope-rules, please visit: https://github.com/scikit-learn-contrib/skope-rules

Course Outline

Introduction

  • Why extract rules from data?

Overview of Sklearn Modules (Decision Tree/Random Forrest)

Installing and Configuring skope-rules

Case Study: Detecting Credit Default Rates

Importing Data

Using SkopeRules for Imbalanced Classification

Training the SkopeRules Classifier

Extracting the Rules

Fusing the Rules

Fitting Classification and Regression Trees to Sub-samples

Selecting Higher Precision Rules

Testing Higher Precision Rules

Summary and Conclusion

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