AI-ASSISTANTS
Copilot's week is model housekeeping and cost accounting, not new capability.
Alternatives · AI assistants
imbalanced-learn alternatives
The best imbalanced-learn alternatives in AI assistants, ranked by Sparkpulse's velocity_score.
Updated Aug 12, 2026
Looking for the best alternatives to imbalanced-learn? Sparkpulse tracks and ranks 12 alternatives in AI assistants by shipping velocity — how frequently each ships meaningful updates, verified from official changelogs. For reference, imbalanced-learn shipped 0 meaningful updates in the last 30 days and carries a velocity score of 0.0 out of 10 in 2026. The alternatives below are ranked the same way, so you're comparing real release momentum, not marketing claims.
About imbalanced-learn
The resampling companion to scikit-learn now ships mostly to stay compatible with it.
imbalanced-learn is at 0.14.2. Four of the six releases in the window exist to track a scikit-learn version — 1.5, 1.7, 1.8 and 1.9 in turn — or NumPy 2.0. The genuine additions are thin: InstanceHardnessCV in 0.14.0 and a clearer SMOTENC error when the categorical encoder collapses categories.
Velocity 0.0 · Last update 1h ago
Top 12 alternatives to imbalanced-learn
Ranked by recent ship velocity. Tap any card for the full editorial breakdown, or pivot to a head-to-head.
AI-ASSISTANTS
A billion monthly users, and a feed running on audience content between launches
AI-ASSISTANTS
DocsBot now lets an AI agent administer DocsBot, not just answer with it
AI-ASSISTANTS
DataRobot launches TokenGrid and spends the rest of the month arguing agents need identity
AI-ASSISTANTS
Mem0 splits agent memory from user memory, then spends a week hardening the plumbing
AI-ASSISTANTS
OpenRouter is moving up the stack, from a routing endpoint to the tooling around it.
AI-ASSISTANTS
Docling keeps widening the funnel: every release adds another format the parser can swallow.
AI-ASSISTANTS
Transformers is becoming a kernel-dispatch layer, and it's breaking APIs to get there
AI-ASSISTANTS
Ships a frontier model roughly monthly, then adds the admin controls weeks later.
AI-ASSISTANTS
Every post is a comparison page, and Pictory is always the answer.
AI-ASSISTANTS
A checkpoint-persistence maintenance train, with the tracing API still being argued over.
AI-ASSISTANTS
Writer sells the agentic marketing org first and the platform second.
imbalanced-learn vs alternatives — shipping velocity at a glance
Velocity score (0–10) and meaningful releases shipped in the last 30 days, from official changelogs. Higher = shipping faster.
| Product | Velocity | Sparks · 30d | Focus areas | Latest release |
|---|---|---|---|---|
| imbalanced-learn (baseline) | 0.0 | 0 | imbalanced-dataresamplingscikit-learn | — |
| GitHub Copilot | 10.0 | 0 | model-lifecyclecost-accountingusage-reporting | — |
| Gemini | 8.8 | 0 | gemini-appadoptionmarketing-content | — |
| DocsBot AI | 7.5 | 2 | admin-mcpagentic-operationsslack | DocsBot Operator + Admin MCP: Let Your AI Agent Manage DocsBot |
| DataRobot | 7.5 | 1 | agentic-aiai-governancegpu-utilization | Stop Rate-Limiting Requests. Start Scheduling Tokens: Introducing DataRobot TokenGrid |
| Mem0 | 6.3 | 1 | ai-memoryvector-storessdk | Node SDK adds agent-scoped memory extraction instructions |
| OpenRouter | 6.3 | 1 | model-routingdeveloper-toolingspend-governance | Ori Harness: The Best Way to Use OpenRouter with Any Harness |
| Docling | 6.3 | 0 | document-parsingocrvlm | — |
| Transformers | 6.3 | 1 | kernel-dispatchbreaking-changesvllm-backend | Kernels go opt-in as T5 and linear attention move to shared backends |
| Claude | 6.3 | 1 | model launchesagentic workflowsenterprise governance | Claude Opus 5 launches at half the price of Fable 5 |
| Pictory | 5.0 | 0 | ai-videocontent-repurposingseo-content | — |
| LangGraph | 5.0 | 0 | agent-frameworkcheckpointingstate-persistence | — |
| Writer | 5.0 | 0 | enterprise-aiagentic-marketingai-visibility | — |
The 12 best imbalanced-learn alternatives, in depth
1. GitHub Copilot · velocity 10.0
Copilot's week is model housekeeping and cost accounting, not new capability.
Its velocity score of 10.0/10 reflects longer-term release cadence.
Where imbalanced-learn leans on imbalanced data, resampling and scikit learn, GitHub Copilot focuses on model lifecycle, cost accounting and usage reporting.
GitHub Copilot and imbalanced-learn have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full GitHub Copilot trajectory → · Compare imbalanced-learn vs GitHub Copilot →
2. Gemini · velocity 8.8
A billion monthly users, and a feed running on audience content between launches.
Its velocity score of 8.8/10 reflects longer-term release cadence.
Where imbalanced-learn leans on imbalanced data, resampling and scikit learn, Gemini focuses on gemini app, adoption and marketing content.
Gemini and imbalanced-learn have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full Gemini trajectory → · Compare imbalanced-learn vs Gemini →
3. DocsBot AI · velocity 7.5
DocsBot now lets an AI agent administer DocsBot, not just answer with it.
Over the last 30 days DocsBot AI shipped 2 meaningful updates vs imbalanced-learn's 0, most recently “DocsBot Operator + Admin MCP: Let Your AI Agent Manage DocsBot”. Its velocity score of 7.5/10 blends that with longer-term release cadence.
Where imbalanced-learn leans on imbalanced data, resampling and scikit learn, DocsBot AI focuses on admin mcp, agentic operations and slack.
Over the last 30 days DocsBot AI has been shipping faster than imbalanced-learn — a point in its favour if release momentum matters to you.
Full DocsBot AI trajectory → · Compare imbalanced-learn vs DocsBot AI →
4. DataRobot · velocity 7.5
DataRobot launches TokenGrid and spends the rest of the month arguing agents need identity.
Over the last 30 days DataRobot shipped 1 meaningful update vs imbalanced-learn's 0, most recently “Stop Rate-Limiting Requests. Start Scheduling Tokens: Introducing DataRobot TokenGrid”. Its velocity score of 7.5/10 blends that with longer-term release cadence.
Where imbalanced-learn leans on imbalanced data, resampling and scikit learn, DataRobot focuses on agentic ai, ai governance and gpu utilization.
Over the last 30 days DataRobot has been shipping faster than imbalanced-learn — a point in its favour if release momentum matters to you.
Full DataRobot trajectory → · Compare imbalanced-learn vs DataRobot →
5. Mem0 · velocity 6.3
Mem0 splits agent memory from user memory, then spends a week hardening the plumbing.
Over the last 30 days Mem0 shipped 1 meaningful update vs imbalanced-learn's 0, most recently “Node SDK adds agent-scoped memory extraction instructions”. Its velocity score of 6.3/10 blends that with longer-term release cadence.
Where imbalanced-learn leans on imbalanced data, resampling and scikit learn, Mem0 focuses on ai memory, vector stores and sdk.
Over the last 30 days Mem0 has been shipping faster than imbalanced-learn — a point in its favour if release momentum matters to you.
6. OpenRouter · velocity 6.3
OpenRouter is moving up the stack, from a routing endpoint to the tooling around it.
Over the last 30 days OpenRouter shipped 1 meaningful update vs imbalanced-learn's 0, most recently “Ori Harness: The Best Way to Use OpenRouter with Any Harness”. Its velocity score of 6.3/10 blends that with longer-term release cadence.
Where imbalanced-learn leans on imbalanced data, resampling and scikit learn, OpenRouter focuses on model routing, developer tooling and spend governance.
Over the last 30 days OpenRouter has been shipping faster than imbalanced-learn — a point in its favour if release momentum matters to you.
Full OpenRouter trajectory → · Compare imbalanced-learn vs OpenRouter →
7. Docling · velocity 6.3
Docling keeps widening the funnel: every release adds another format the parser can swallow.
Its velocity score of 6.3/10 reflects longer-term release cadence.
Where imbalanced-learn leans on imbalanced data, resampling and scikit learn, Docling focuses on document parsing, ocr and vlm.
Docling and imbalanced-learn have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full Docling trajectory → · Compare imbalanced-learn vs Docling →
8. Transformers · velocity 6.3
Transformers is becoming a kernel-dispatch layer, and it's breaking APIs to get there.
Over the last 30 days Transformers shipped 1 meaningful update vs imbalanced-learn's 0, most recently “Kernels go opt-in as T5 and linear attention move to shared backends”. Its velocity score of 6.3/10 blends that with longer-term release cadence.
Where imbalanced-learn leans on imbalanced data, resampling and scikit learn, Transformers focuses on kernel dispatch, breaking changes and vllm backend.
Over the last 30 days Transformers has been shipping faster than imbalanced-learn — a point in its favour if release momentum matters to you.
Full Transformers trajectory → · Compare imbalanced-learn vs Transformers →
9. Claude · velocity 6.3
Ships a frontier model roughly monthly, then adds the admin controls weeks later.
Over the last 30 days Claude shipped 1 meaningful update vs imbalanced-learn's 0, most recently “Claude Opus 5 launches at half the price of Fable 5”. Its velocity score of 6.3/10 blends that with longer-term release cadence.
Where imbalanced-learn leans on imbalanced data, resampling and scikit learn, Claude focuses on model launches, agentic workflows and enterprise governance.
Over the last 30 days Claude has been shipping faster than imbalanced-learn — a point in its favour if release momentum matters to you.
Full Claude trajectory → · Compare imbalanced-learn vs Claude →
10. Pictory · velocity 5.0
Every post is a comparison page, and Pictory is always the answer.
Its velocity score of 5.0/10 reflects longer-term release cadence.
Where imbalanced-learn leans on imbalanced data, resampling and scikit learn, Pictory focuses on ai video, content repurposing and seo content.
Pictory and imbalanced-learn have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full Pictory trajectory → · Compare imbalanced-learn vs Pictory →
11. LangGraph · velocity 5.0
A checkpoint-persistence maintenance train, with the tracing API still being argued over.
Its velocity score of 5.0/10 reflects longer-term release cadence.
Where imbalanced-learn leans on imbalanced data, resampling and scikit learn, LangGraph focuses on agent framework, checkpointing and state persistence.
LangGraph and imbalanced-learn have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full LangGraph trajectory → · Compare imbalanced-learn vs LangGraph →
12. Writer · velocity 5.0
Writer sells the agentic marketing org first and the platform second.
Its velocity score of 5.0/10 reflects longer-term release cadence.
Where imbalanced-learn leans on imbalanced data, resampling and scikit learn, Writer focuses on enterprise ai, agentic marketing and ai visibility.
Writer and imbalanced-learn have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full Writer trajectory → · Compare imbalanced-learn vs Writer →
Frequently asked questions
What are the best alternatives to imbalanced-learn?
The top imbalanced-learn alternatives we currently track in AI assistants are GitHub Copilot, Gemini, DocsBot AI, DataRobot, Mem0, ranked by recent ship velocity.
How is this list of imbalanced-learn alternatives ranked?
Alternatives are ranked by Sparkpulse's velocity_score — release cadence + 30-day spark count + sector-relative ship rate.
Can I compare imbalanced-learn directly with one of these alternatives?
Yes — every card has a "Compare with imbalanced-learn" link to a side-by-side /compare page.