Summary of Alice: Combining Feature Selection and Inter-rater Agreeability For Machine Learning Insights, by Bachana Anasashvili et al.
ALICE: Combining Feature Selection and Inter-Rater Agreeability for Machine Learning Insights
by Bachana Anasashvili, Vahidin Jeleskovic
First submitted to arxiv on: 13 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC); Applications (stat.AP); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Automated Learning for Insightful Comparison and Evaluation (ALICE) library in Python merges feature selection with inter-rater agreeability to gain insights into black box Machine Learning models. The framework builds upon an overview of interpretability in ML, detailing its architecture and intuition. Initial experiments on a customer churn predictive modeling task demonstrate the efficacy of ALICE, with results presented alongside potential future avenues for exploration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new Python library called ALICE, which helps us understand how Machine Learning models work. It combines two ideas: selecting important features and measuring how well different people agree on something. The authors explain their framework in detail and show initial results from testing it on a customer prediction problem. You can find the code and experiment notes online. |
Keywords
* Artificial intelligence * Feature selection * Machine learning
