dingxingdi Skill: competition-style-ml-engineering
1. Capability Definition & Real Case
- Professional Definition: The ability to solve benchmarked ML engineering tasks in competition-like settings that require working from datasets, descriptions, grading code, and submission formats to produce valid, high-scoring outputs under resource and process constraints.
- Dimension Hierarchy: Data and ML Workflow Engineering->Machine Learning Engineering->competition-style-ml-engineering
Real Case
[Case 1]
- Initial Environment: An offline competition workspace contains a description, train.csv, test.csv, sample_submission.csv, grading code, and baseline utilities. The task has no complete working solution yet.
- Real Question: Produce a valid submission that performs competitively on the competition metric using only the provided resources.
- Real Trajectory: Read the competition overview and data schema, inspect the submission format, implement preprocessing and a baseline model, validate the submission locally, iterate on data handling and model choices, and generate the final CSV file.
- Real Answer: A valid submission file is produced in the expected format and scores better than naive baselines under the local grader.
- Why this demonstrates the capability: This tests competition-style ML engineering because the agent must combine dataset understanding, code writing, submission validity, and iterative optimization inside a leaderboard-like workflow rather than merely patching an existing training script.
[Case 2]
- Initial Environment: A more complex competition workspace includes multimodal data, longer training cycles, and stricter formatting rules. Local validation can confirm file validity but not reveal the full score directly.
- Real Question: Engineer a robust submission pipeline that can train, evaluate, and export predictions in the required competition format.
- Real Trajectory: Map data modalities and labels, build a training script, generate validation estimates internally, check submission validity with the local tool, debug formatting or shape errors, and refine the strongest model within runtime constraints.
- Real Answer: The pipeline yields a valid competition submission and demonstrates sound engineering choices for improving performance under limited feedback.
- Why this demonstrates the capability: The capability lies in handling the entire competition workflow: datasets, local validation, formatting, experiment planning, and robustness to hidden-score uncertainty. This goes beyond ordinary model improvement because correctness of the submission process itself is part of the task.
Pipeline Execution Instructions
To synthesize data for this capability, you must strictly follow a 3-phase pipeline. Do not hallucinate steps. Read the corresponding reference file for each phase sequentially:
Phase 1: Environment Exploration Read the exploration guidelines to discover raw knowledge seeds:
references/EXPLORATION.mdPhase 2: Trajectory Selection Once Phase 1 is complete, read the selection criteria to evaluate the trajectory:
references/SELECTION.mdPhase 3: Data Synthesis Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data:
references/SYNTHESIS.md