svm
Use this model doc whenever the user wants to perform disease classification with SVM. This is a non-deep-learning supervised route focused on neuroimaging-based case-control prediction from ROI-wise or tabular features.
npx skills add BioTender-max/awesome-bio-agent-skills --skill svm --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# SVM Model Doc ## Overview SVM is a classical non-deep-learning method for neuroimaging-based disease classification. - Model family: non-deep-learning supervised classification method - Typical objectives: - classify patient vs control groups from neuroimaging features - build discriminative models from ROI features or tabular summaries - export predictive scores and evaluation metrics - Primary input: preprocessed fMRI / sMRI derived features, labels, optional covariates - Primary output: class predictions, decision scores, cross-validation metrics In NeuroClaw, this document is model-level guidance for SVM-based disease classification workflows rather than deep learning phenotype prediction. Upstream preparation should usually be delegated to: - `fmri-skill` for fMRI preprocessing and ROI / voxel feature preparation - `smri-skill` for structural feature extraction when disease classification uses sMRI - `nilearn-tool` for concrete SVM fitting on prepared feature tables **Research use only.** --- ## Quick Start ### 1) Prepare disease classification inputs Expected inputs: - subject-level labels such as patient / control - preprocessed imaging features - optional covariates such
- Overview
- Quick Start
- 1) Prepare disease classification inputs
- 2) SVM route
- Input / Output Contract
- Required inputs
- Optional inputs
- Produced outputs
- Recommended Delegation
- When to Use SVM
- Limitations and Notes
- Reference
delegated through claw-shell after features are prepared python skills/nilearn-tool/scripts/svm_classifier_reference.py \
What does the svm skill do?
Use this model doc whenever the user wants to perform disease classification with SVM. This is a non-deep-learning supervised route focused on neuroimaging-based case-control prediction from ROI-wise or tabular features.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill svm --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.