AI Tool Profile
scikit-learn
A Python library for predictive modeling and model evaluation.
Before you buy
Best for: Developers evaluating classical machine-learning pipelines on structured data.
Price: Canada/CAD: C$0 BSD-3-Clause software license
Included: Estimators, preprocessing, model selection, metrics and pipeline building blocks.
Main limits: Avoid leakage and evaluate on realistic held-out data. The demo target is exactly linear by construction, so its near-zero error has no forecasting significance.
What Kingy showed: LinearRegression fits 18 fictional rows and predicts six held-out rows from an exactly generated linear target. The recorded error is only a software-path check.
Buying details checked 2026-10-23 with region and currency stated above. Historical demonstrations and the broader fact ledger keep their own dates.
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Coverage describes this profile, not a product score or a current price guarantee.
Kingy AI Product Facts
Unknown
Current statusUnknown
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- Latest source check
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Sources and checks
- Coverage
- 0 of 87 tracked fields currently verified
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Source-check dates apply to the tracked facts. Editorial updates elsewhere on this page may have a different date.
Plans and pricing units
Not yet structured in this Kingy record.
Where it runs
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Stack and integrations
- Model / provider dependencies
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Data and regions
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Launch and latest material update
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Review history
- scikit-learn — Product event recorded
Kingy published an approved update for a sourced product event.
View published revision
Which plan is worth considering
Canada/CAD: C$0 BSD-3-Clause software license. Estimators, preprocessing, model selection, metrics and pipeline building blocks.
Limits that affect the purchase
Avoid leakage and evaluate on realistic held-out data. The demo target is exactly linear by construction, so its near-zero error has no forecasting significance. The local software license costs C$0. Hardware, developer time, hosting and optional commercial services are separate. The package API is included. This is local developer access, not a paid AI-model API allowance or a hosted subscription.
What the demonstration shows
LinearRegression fits 18 fictional rows and predicts six held-out rows from an exactly generated linear target. The recorded error is only a software-path check. This fresh local test used scikit-learn 1.9.1 on October 23, 2026 with fictional campaign data. It is separate from the related historical article. The source, output and checks are downloadable. See the fresh local demonstration.
Sources and buying-review date
Buying details checked October 23, 2026. License and feature research uses official sources. Demonstration descriptions refer to the newly executed local tests and published source package; all campaign numbers are fictional. No new subscription, paid generation or checkout was performed. The buying-review date does not renew every claim in the broader facts ledger.
- Official plans and current offer
- Fresh demonstration and limits
- Reproduction source package
- Package release and license
- Related Kingy article
- Fresh local demonstration
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