Svm Model Importance Analysis

OrganizationPopular

aipoch

svm-model-importance-analysis

Use when you need a standardized R CLI workflow to run two-class SVM-RFE feature ranking on an expression-like matrix, choose an informative feature count from cross-validated error, and generate reproducible ranking and error plots. NOT for regression, multi-class classification, missing-value imputation, or remote data fetching.

Overview

Publisher	aipoch
Repository	`medical-research-skills`
Skill name	`svm-model-importance-analysis`
Stars	1.9K
Forks	188
Bundled files	21
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
21 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by aipoch on GitHub. Read the source before you install it.

Installation

Install the Svm Model Importance Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/svm-model-importance-analysis
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/svm-model-importance-analysis

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Svm Model Importance Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Svm Model Importance Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Svm Model Importance Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Source: https://github.com/aipoch/medical-research-skills

SVM Model Importance Analysis

When to Read External Files

Situation	File to Read	Purpose
Need algorithm details	`references/algorithm.md`	Explain SVM-RFE ranking, cross-validation logic, assumptions, and interpretation
Need to execute the analysis	`scripts/main.R`	Run the CLI entry point with a complete `Rscript` command
Encounter an error	`references/troubleshooting.md`	Map standardized error codes to causes and fixes
Need CLI examples	`references/cli-guide.md`	Review installation steps and runnable CLI examples
Need a runnable smoke test	`tests/data/`	Use the bundled small dataset for verification

Usage

bash
Rscript scripts/main.R \
  --input_file ./input/expression_matrix.csv \
  --group_file ./input/group_info.csv \
  --case_group Case \
  --control_group Control \
  --output_dir output/basic-run \
  --seed 42 \
  --timeout_seconds 600

Arguments

Short	Long	Type	Default	Required	Description
`-i`	`--input_file`	character	none	yes, unless `--plot_only TRUE`	Expression matrix file with samples in rows and features in columns
`-g`	`--group_file`	character	none	yes, unless `--plot_only TRUE`	Group file with sample IDs in the first column
`-c`	`--case_group`	character	none	yes, unless `--plot_only TRUE`	Case group label
`-r`	`--control_group`	character	none	yes, unless `--plot_only TRUE`	Control group label
`-o`	`--output_dir`	character	`output`	yes	Output directory inside the skill root
`-p`	`--plot_only`	logical	`FALSE`	no	Reuse `output_dir/data/svm_result.rds` and regenerate plots without rerunning SVM-RFE
`-s`	`--seed`	integer	`42`	no	Random seed for reproducibility
`-t`	`--timeout_seconds`	integer	`600`	no	Elapsed time limit for the run
	`--svm_k`	integer	`10`	no	Number of stratified outer folds used for SVM-RFE and validation
	`--svm_halve_above`	integer	`50`	no	If surviving features exceed this count, remove half per iteration
	`--svm_max_features_cap`	integer	`30`	no	Maximum feature count evaluated on the error curve
	`--svm_select_rule`	character	`min`	no	Feature-count rule: `min` or `tolerance`
	`--svm_tol`	numeric	`0.01`	no	Tolerance used when `--svm_select_rule tolerance` is selected
	`--svm_error_height`	numeric	`5`	no	SVM error plot height in inches
	`--svm_error_width`	numeric	`6`	no	SVM error plot width in inches
	`--svm_error_xlab`	character	`Number of Features`	no	X-axis label for the SVM error plot
	`--svm_error_ylab`	character	`Classification Error Rate`	no	Y-axis label for the SVM error plot
	`--svm_error_main_line_color`	character	`black`	no	Main line color for the SVM error plot
	`--svm_error_second_line_color`	character	`#2BA2DE`	no	Baseline line color for the SVM error plot
	`--svm_error_best_point_color`	character	`red`	no	Highlight color for the best feature-count point
	`--svm_error_noinfo_lty`	integer	`3`	no	Line type for the no-information baseline
	`--svm_error_label_cex`	numeric	`0.75`	no	Label size for the best-point annotation
	`--svm_error_label_pos`	integer	`4`	no	Label position for the best-point annotation
	`--svm_rank_top_n`	integer	`20`	no	Maximum number of ranked features shown in the ranking plot
	`--svm_rank_width`	numeric	`7`	no	Ranking plot width in inches
	`--svm_rank_height`	numeric	`6`	no	Ranking plot height in inches
	`--svm_rank_color`	character	`#2BA2DE`	no	Bar color for the ranking plot
	`--svm_rank_title`	character	`SVM-RFE Feature Ranking`	no	Title for the ranking plot

Input Format

Expression Matrix

CSV or TSV.
First column: sample IDs.
Remaining columns: numeric features.
Samples must be rows.
Missing or non-numeric feature values are not allowed.

Example:

csv
sample,HIF1A,NR4A1,SOCS1
S1,6.21,-1.34,2.01
S2,6.57,0.37,3.62
S3,7.05,2.12,5.01

Group File

CSV or TSV.
First column: sample IDs.
One additional column must contain both the case and control labels.
Exactly two groups are supported.

Example:

csv
sample,group
S1,Case
S2,Case
S3,Control

Output Files

File	Format	Description
`data/svm_result.rds`	RDS	Serialized SVM-RFE bundle with ranking results and metadata
`table/svm_rfe_features.csv`	CSV	Selected ranked features using the chosen feature-count rule
`table/svm_rfe_full_ranking.csv`	CSV	Full ranking table across all input features
`plot/svm_rfe_error_plot.pdf`	PDF	Cross-validated classification error across feature counts
`plot/svm_rfe_ranking_plot.pdf`	PDF	Bar plot of the highest-ranked SVM-RFE features
`session_info.txt`	TXT	R version, platform, and package version information

Error Handling

Successful runs exit with status code 0.
Failed runs exit with status code 1.
Error messages use standardized names such as SKILL_FILE_NOT_FOUND and SKILL_INVALID_PARAMETER.
Output paths are validated so that --output_dir cannot write outside the skill root.
The analysis never performs network requests and never executes user input through eval(), exec(), or system().

Common codes:

Error Code	Meaning
`SKILL_FILE_NOT_FOUND`	An input file or required plot-only artifact does not exist
`SKILL_MISSING_COLUMNS`	The input file does not contain the required columns
`SKILL_EMPTY_DATA`	An input file is empty or a required ranking table is unavailable
`SKILL_INVALID_PARAMETER`	A CLI argument, group setting, numeric constraint, or path is invalid
`SKILL_SAMPLE_MISMATCH`	Sample IDs do not match between the expression matrix and group file
`SKILL_PACKAGE_NOT_FOUND`	One or more required CRAN packages are missing

For detailed fixes, READ: references/troubleshooting.md

Testing

Help Check

bash
Rscript scripts/main.R --help

Full Test Run

bash
Rscript tests/run_tests.R

Direct Test Command

bash
Rscript scripts/main.R \
  --input_file tests/data/expression_matrix.csv \
  --group_file tests/data/group_info.csv \
  --case_group AR \
  --control_group Control \
  --output_dir tests/output/manual-test \
  --seed 42 \
  --svm_k 4 \
  --svm_max_features_cap 6 \
  --svm_rank_top_n 6 \
  --timeout_seconds 300

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Svm Model Importance Analysis AI skill do?

Why use Svm Model Importance Analysis on TypingMind?

Because you install it once and use it with any model. Svm Model Importance Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Svm Model Importance Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/svm-model-importance-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Svm Model Importance Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Svm Model Importance Analysis?

As many as you like. As long as a model supports skills, you can use Svm Model Importance Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Svm Model Importance Analysis AI skill free?

Yes. It is published on GitHub by aipoch under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Use it in TypingMind

Works with any AI model

Skill instructions

SVM Model Importance Analysis

When to Read External Files

Usage

Arguments

Input Format

Expression Matrix

Group File

Output Files

Error Handling

Testing

Help Check

Full Test Run

Direct Test Command

Bundled files

Frequently asked questions

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