IT AI Index

Index › Vendors › scikit-learn · September 2026 Edition

1 category · Named, not ranked

scikit-learn

10Judge labels

1First choices

0Negative labels

5 of 12Models named it

1Category

September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.2, every buyer segment counted.

Standing

7 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. scikit-learn was named 7 times in ML platforms, where Azure Machine Learning led with 17%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small business42 of 124

under the label threshold

Mid-market14 of 99

under the label threshold

Enterprisenot named

No model named it for this buyer.

In ml platforms · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named scikit-learn for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.

Category	Function	Share	Rank	Negative rate	Labels	Quadrant
ML platforms	Data platform	2%	14 of 99	0%	7	under 10 labels · led by Azure Machine Learning at 17%

Movement

This is the first edition on this tier, so no move can be computed for scikit-learn yet. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated scikit-learn across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

Model	First choice	Alternative	Mention	Labels
Claude Haiku 4.5	1	0	0	1
GPT-5.4 mini	0	0	0	0
Gemini 3.5 Flash	0	0	0	0
Perplexity Sonar	0	1	0	1
Grok 4.1 Fast	0	0	0	0
Mistral Small	0	0	0	0
DeepSeek V4 Flash	0	1	1	2
Llama 4 Maverick	0	0	0	0
Qwen 3.7 Flash	0	0	0	0
Kimi K2	0	2	0	2
GLM 4.7 FlashX	0	1	0	1
MiniMax M2.5	0	0	0	0

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.

FramingLabels by classFirst choices

Direct1 labelNone

Paraphrase0 labelsNone

Comparative2 labelsNone

Budget-constrained6 labels1

Scale-constrained0 labelsNone

Negative1 labelNone

First choiceAlternativeMentionNegative10 labels in all, every segment counted; 1 of the 1 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

“open-source tools like TensorFlow or Scikit-learn” Claude Haiku 4.5 · ML platforms · budget prompt · first choice

“Start with Scikit-learn if you're new to ML and working on traditional ML problems” Kimi K2 · ML platforms · comparative prompt · alternative

“Best if your team can code, because these are free open-source libraries” Perplexity Sonar · ML platforms · budget prompt · alternative

“scikit-learn \u2013 Traditional ML algorithms, simple API” GLM 4.7 FlashX · ML platforms · budget prompt · alternative

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

No model argued against it.

Named alongside

The products named in the same answers as scikit-learn, over the 10 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and scikit-learn was named but was not.

ProductSame answerTook the first choice insteadHead to head

PyTorch8 of 100Not in the top three

Azure Machine Learning7 of 101Not in the top three

Google Vertex AI7 of 101Not in the top three

TensorFlow7 of 100Not in the top three

Amazon SageMaker6 of 101Not in the top three

MLflow5 of 101Not in the top three

Google Colab4 of 102Not in the top three

Databricks4 of 100Not in the top three

KNIME Analytics Platform3 of 102Not in the top three

KNIME3 of 101Not in the top three

A head-to-head page exists where both products are in a category's top three. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named scikit-learn. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 8 of the 10 answers that named scikit-learn and are not a share of its labels.

Domains cited

g2.com6

aimultiple.com4

deepusecase.com4

godofprompt.ai4

softwr.com4

modern-datatools.com3

spotsaas.com3

startupstash.com3

articsledge.com2

digitalocean.com2

Thirty-five of the thirty-five domain citations in answers naming scikit-learn came from somebody else's page.

Pages cited

g2.com/discussions/what-is-the-most-cost-efficient-machine-learning-platform5

aimultiple.com/open-source-ai-platforms4

godofprompt.ai/blog/machine-learning-platforms-for-small-businesses4

softwr.com/best/machine-learning-for-small-businesses4

spotsaas.com/list/data-science-and-machine-learning-platforms/for-small-business3

startupstash.com/best-low-code-machine-learning-platforms3

articsledge.com/post/machine-learning-platforms-comparison-azure-ai-vs-aws-sagemaker-vs-go2

deepusecase.com/blog/machine-learning-software-comparison-20242

Pages are listed as the models cited them.

Names read as scikit-learn

What the judge wrote, as written, with how often. The vendor table decides that these count as scikit-learn; a claim can dispute any of them.

Scikit-learn 3

Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when scikit-learn's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as scikit-learn, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at scikitlearn.com is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.