Create ML Model

[PredictabilityAtScale]

Summary

Create an initial predictive model based on the exported JSON of closed issue analysis, predict the risk of a new proposed issues based on its title and summary.

Metadata

• Priority: Unspecified
• Labels: feature, machine learning

Similar Issues

• No related closed issues surfaced.

Captured Keywords

feature, change, update, improvement, task

Created via IssueTriage new issue flow.

[PredictabilityAtScale]

IssueTriage Assessment

Model: openai/gpt-5-mini
Issue: PredictabilityAtScale/IssueTriage #17
View issue

Dimension	Score
Composite	67.2
Requirements	72.5
Complexity	48.0
Security	90.0
Business Impact	65.0

Summary: Request to build an initial predictive model from an exported JSON to predict issue risk from title and summary is clear at a high level. Critical details are missing: the dataset location, the target label definition of “risk,” and the expected v1 delivery format. These omissions prevent an autonomous agent from training and delivering a working v1. Answering up to three targeted questions will enable a complete training, evaluation, and inference artifact to be produced.

Pre-implementation questions:

• Where is the exported JSON dataset located (repo path or a download URL) so the agent can access training data?
• Which field in the dataset is the target label and how is “risk” defined (binary/categorical/continuous and class meanings)?
• What output do you expect for v1: a saved model + prediction script, a CLI command, or a simple REST/HTTP prediction endpoint?

Last updated: 11/3/2025, 9:20:45 AM

[PredictabilityAtScale]

IssueTriage Assessment

Model: openai/gpt-5-mini
Issue: PredictabilityAtScale/IssueTriage #17
View issue

Dimension	Score
Composite	67.2
Requirements	72.5
Complexity	48.0
Security	90.0
Business Impact	65.0

Summary: The request to build an initial predictive model from an exported JSON to estimate issue “risk” from title and summary is clear at a high level. Key missing details prevent an autonomous agent from delivering a working v1: the dataset location, the exact target label definition for “risk,” and the expected v1 output format. Once those are provided an agent can train, evaluate, and produce inference artifacts. No major security or integration risks are evident.

Pre-implementation questions:

• Where is the exported JSON dataset located (repository path or a direct download URL) so the agent can access training data?
• Which field in the dataset is the target label and how is “risk” defined (binary/categorical/continuous and the meaning of each class/value)?
• What do you expect for v1 delivery: a saved model plus a prediction script, a CLI command, or a simple REST/HTTP prediction endpoint?

Last updated: 11/3/2025, 4:03:05 PM

[PredictabilityAtScale]

IssueTriage Assessment

Model: openai/gpt-5-mini
Issue: PredictabilityAtScale/IssueTriage #17
View issue

Dimension	Score
Composite	67.2
Requirements	72.5
Complexity	48.0
Security	90.0
Business Impact	65.0

Summary: Clear high-level request to build an initial predictive model from an exported JSON to predict issue risk from title and summary. Critical missing details — dataset location, the exact definition of the “risk” target label, and the expected v1 output format — prevent an autonomous agent from delivering a working v1. Once those are provided, an agent can handle training, evaluation, and produce inference artifacts. No major security risks are apparent for a first-pass model.

Pre-implementation questions:

• Where is the exported JSON dataset located (repo path or a direct download URL) so the agent can access training data?
• Which field in the dataset is the target label and how is “risk” defined (binary/categorical/continuous and the meaning of each class/value)?
• What should v1 deliver: a saved model plus a prediction script, a CLI command, or a simple REST/HTTP prediction endpoint?

Last updated: 11/11/2025, 5:44:07 PM

[PredictabilityAtScale]

IssueTriage Assessment

Model: openai/gpt-5-mini
Issue: PredictabilityAtScale/IssueTriage #17
View issue

Dimension	Score
Composite	50.2
Requirements	30.0
Complexity	48.0
Security	90.0
Business Impact	65.0

Summary: The issue requests an initial predictive model to estimate issue “risk” from title and summary using an exported JSON—high-level intent is clear. Critical information is missing (where the dataset is, how “risk” is defined, and the desired v1 output), which prevents an autonomous agent from producing a working v1. No major security concerns are apparent for a first-pass model. Answering up to three targeted questions will unblock training, evaluation, and inference artifacts.

Pre-implementation questions:

• Where is the exported JSON dataset located (repo path or direct download URL) so the agent can access training data?
• Which field in the dataset is the target label and how is “risk” defined (binary/categorical/continuous and the meaning of each class/value)?
• What should v1 deliver: a saved model plus a prediction script, a CLI command, or a simple REST/HTTP prediction endpoint?

Last updated: 11/13/2025, 1:51:35 PM