Machine Learning

Interpretation

Weiyuan @ SFU DSL

Machine learning becomes popular in lots of domains.

  • Learned Index

Machine learning becomes popular in lots of domains.

\(\gamma((R \bowtie S) \bowtie T\)

\(\gamma(R \times S) \bowtie T\)

VS

  • Learned Index
  • Learned Query Plan

Machine learning becomes popular in lots of domains.

\(\gamma((R \bowtie S) \bowtie T\)

\(\gamma(R \times S) \bowtie T\)

VS

  • Learned Index
  • Learned Query Plan

Postgres Plan: 18.3s

Learned Plan: 3.9s

Machine learning becomes popular in lots of domains.

\(\gamma((R \bowtie S) \bowtie T\)

\(\gamma(R \times S) \bowtie T\)

VS

  • Learned Index
  • Learned Query Plan

Avoid Cartesian Products is a Common Heuristic!

Postgres Plan: 18.3s

Learned Plan: 3.9s

Machine learning becomes popular in lots of domains.

  • Learned Index
  • Learned Query Plan
  • Learned DB Parameter

Machine learning becomes popular in lots of domains.

  • Learned Index
  • Learned Query Plan
  • Learned DB Parameter
  • ER
  • ....

EU General Data Protection Regulation 

shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her“.

Not Understand & Important

Interpretation

Because of

  • Safety: We want to make sure the system is making sound decisions.
  • Debugging: We want to understand why a system doesn't work, so we can fix it.
  • Science: We want to understand something new.
  • Legal/Ethics: We're legally required to provide an explanation and/or we don't want to discriminate against particular groups.
  • ...
  • Case-based
  • Feature-based
  • Model-based

Types of interpretations

  • Case-based
  • Feature-based
  • Model-based

Types of interpretations

  • Case-based
  • Feature-based
  • Model-based

Types of interpretations

  • Safety: We want to make sure the system is making sound decisions.
  • Debugging: We want to understand why a system doesn't work, so we can fix it.
  • Science: We want to understand something new.
  • Legal/Ethics: We're legally required to provide an explanation and/or we don't want to discriminate against particular groups.
  • ...
  1. If there is an issue with the data
  2. If so, where the issues are
  3. What the issue is
  4. How to fix.

Thanks

Copy of Model Interpretibility

By Weiyüen Wu

Copy of Model Interpretibility

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