i.svm.predict(1)

💖 NAME 💖

okay bestie, welcome to i.svm.predict(1) from the GRASS User's Manual — yes, the manual name is right there, no cap. This module is literally Predict with a SVM and also Predict with a Support Vector Machine (same icon energy, longer name 💅).

✨ KEYWORDS ✨

imagery, svm, classification, prediction, regression

🦋 SYNOPSIS 🦋

i.svm.predict
i.svm.predict --help
i.svm.predict group=name  [subgroup=name]  signaturefile=name
output=name  [cache=cache size]   [--overwrite]  [--help]  [--verbose]
[--quiet]  [--ui]

🚩 Flags

• --overwrite — Allow output files to overwrite existing files. (Use with intention, bestie 😭)
• --help — Print usage summary.
• --verbose — Verbose module output. Give me all the details, periodt.
• --quiet — Quiet module output. Shhh era.
• --ui — Force launching GUI dialog.

🎀 Parameters

• group=name [required] — Maps with feature values (attributes). This is the feature-value situation.
• subgroup=name — Name of input imagery subgroup.
• signaturefile=name [required] — Name of input file containing signatures.
• output=name [required] — Output map with predicted class or calculated value.
• cache=cache size — LIBSVM kernel cache size in MB. Options: 1-; Default: 512.

🌸 DESCRIPTION 🌸

i.svm.predict is the prediction girlie: it predicts values with a Support Vector Machine (SVM) and stores them in a raster file. Predictions are based on a signature file generated with i.svm.train.

Internally the module performs input value rescaling of each of imagery group rasters by minimum and maximum range determined during training. That's the internal glow-up ✨.

🔥 NOTES 🔥

i.svm.train internally is using the LIBSVM. For introduction into value prediction or estimation with LIBSVM, see a Practical Guide to Support Vector Classification by Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin.

It is strongly suggested to have semantic labels set for each raster map in the training data (feature value) and in value prediction imagery groups. Use r.support to set semantic labels. (Semantic labels are the vibe check here, bestie.)

👑 PERFORMANCE 👑

Value prediction is done cell by cell and thus memory consumption should be constant. Lowkey efficient.

The cache parameter determines the maximum memory allocated for kernel caching to enhance computational speed. It’s important to note that the actual module’s memory consumption may vary from this setting, as it solely impacts LIBSVM’s internal caching. The cache is utilized on an as-needed basis, so it’s unlikely to reach the specified value. (So it's giving “I’ll help if you need me,” not “I’m eating all the RAM” 💀)

🍓 EXAMPLE 🍓

This is the second part of classification process. See i.svm.train for the first part.

Predict land use classes form a LANDSAT scene from October of 2002 with a SVM trained on a 1996 land use map landuse96_28m.

i.svm.predict group=lsat7_2002 subgroup=res_30m \
    signaturefile=landuse96_rnd_points output=pred_landuse_2002

🌈 SEE ALSO 🌈

• Train SVM: i.svm.train
• Set semantic labels: r.support
• Other classification modules: i.maxlik, i.smap
• LIBSVM home page: LIBSVM - A Library for Support Vector Machines

🧚 REFERENCES 🧚

Please cite both - LIBSVM and i.svm.

• For i.svm.* modules: Nartiss, M., & Melniks, R. (2023). Improving pixel-based classification of GRASS GIS with support vector machine. Transactions in GIS, 00, 1–16. https://doi.org/10.1111/tgis.13102
• For LIBSVM: Chang, C.-C., & Lin, C.-J. (2011). LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27.

💜 AUTHOR 💜

Maris Nartiss, University of Latvia.

🫶 SOURCE CODE 🫶

Available at: i.svm.predict source code (history)

Accessed: Saturday Oct 31 11:14:19 2026

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© 2003-2026 GRASS Development Team, GRASS 8.5.0 Reference Manual

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okay bestie, that's the whole page. Signing off from GRASS 8.5.0, accessed Saturday Oct 31 11:14:19 2026 💅✨