14 hours (usually 2 days including breaks)
- Python programming experience
- Knowledge of machine learning algorithms
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.
- 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
- Why extract rules from data?
Overview of Sklearn Modules (Decision Tree/Random Forrest)
Installing and Configuring skope-rules
Case Study: Detecting Credit Default Rates
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
I like to combine the actual ML application scenarios.