ai-agent

Boost Model Rankings with IJCAI17_Tianchi_Rank4

Name: Boost Model Rankings with IJCAI17_Tianchi_Rank4
Author: Jessicamidi

Use IJCAI17_Tianchi_Rank4 to improve ranking algorithms in Python, ideal for founders building AI-driven recommendation or search systems.

202 stars134 forksPythonGuide quality 8/10 · reviewed Oct 12Updated 8/26/2017100% free · open source

What it does

IJCAI17_Tianchi_Rank4 is a Python-based AI agent that participates in the IJCAI-17 Tianchi competition, focusing on a specific task with a ranking of 4

When to use it

•When you need to analyze and act on data from the IJCAI-17 Tianchi competition
•For tasks that require ranking or competition-based AI decision-making
•In situations where Python-based AI agents are preferred or required

Ready-to-paste prompt

python main.py --task competition --mode train --dataset tianchi

Heads up: The agent requires a specific version of Python (3.x) and dependencies listed in the requirements.txt file, which must be installed manually or using pip install -r requirements.txt

Saves to your device

Use with Claude

New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

✅ Light setup

Installs with a command or two; your AI agent can do it for you.

Try it instantly — no install

Claude Code

mkdir -p ~/.claude/skills/ijcai17-tianchi-rank4 && curl -fsSL https://workflowstacks.com/api/skills/ijcai17-tianchi-rank4/claude-skill -o ~/.claude/skills/ijcai17-tianchi-rank4/SKILL.md

Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)

claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How Boost Model Rankings with IJCAI17_Tianchi_Rank4 works

Codeflow

Free to inspect

Boost Model Rankings with IJCAI17_Tianchi_Rank4 is a small Python project (~2.2k lines across 14 code files). Setup is light: small project — see the README for how to run it. Last commit 112 months ago, MIT license, has a test suite.

Size

Small codebase

~2.2k lines · 14 code files · 19 min skim

Setup

Light setup

Small project — see the README for how to run it.

Runs on

Python

No API keys detected

Python 100%

Where to start reading

1
README.md
Start here — what it does and how to install it

What's in each folder

model/Data models & types13 files

additional/Folder7 files

feature/Folder7 files

TOOLS/Helper scripts2 files

data/Data files2 files

data_new/Folder2 files

READMEHas testsDocumentedNo CIMIT licenseLast update 112 mo ago