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# System prepended metadata

title: Results from BlockScience
tags: [Draft]

---

---
tags: Draft
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# Results from BlockScience

## 

Goals:
- Show that we have a semi-supervised ML pipeline for detecting sybil attack
- Explain what that entails (options)
    - White box algorithms
    - Feature Engineering
    - Sensitivity vs Specificity
    - Labels and GIGO
    - Simple models vs ensemble models
- Impress with some numbers
- Impress with some visualizations
- Convince the audience that will only get better through continous evaluation

Target Audience: 
- 

Boundary conditions:
- 5min for presentation, 2min for Q&A
- No doxing
- No sharing of feature specifics


## Outline

- Intro (45s)
    - "Hi! It is well known that we had lots of attacks. We from BSci together with a tight collaboration with the Gitcoin Dev Team have deved a Semi Supervised Piepline"
- Semi Supervised Pipeline (3m)
    - What is a semi supervised pipeline (45s)
    - What are the pieces of the pipeline (1min)
        - White box algo (40s)
        - Labels and GIGO (20s)
    - How it gets better with time (1min)
        - Feature Engineering (15s)
        - Human evaluation (30s)
        - Simple & ensemble models (15s)
    - What we have now (20s)
        - Mixture of DTs and RFs
- Results (1min)
    - Summary stats from EDA (20s)
    - Prediction results (20s)
    - Predictive features (20s)
- Conclusions (30s)
    - 
- Questions (2min)


## Content resources

