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Add benchmarks

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
35/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
python
Domain
machine-learning, performance

Research direction

Review the repository structure and compare the benchmark coverage in https://github.com/hcho3/fast_svmlight_loader. Determine how classification, multilabel, and regression workloads should be represented, then add benchmarks covering those categories. Done means all three workload types have repeatable benchmark results and the benchmark setup is documented.

Written by the indexing model from the issue text.

Description

Dominant language
Python
Stars
2
Forks
0
No merged PRs in 30d

Getting set up

This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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