Sklearn SVMInstall

Summary

Train an SVM model (classifier or regressor) using scikit-learn.

Inputs

• main (required) — Input items each containing the feature columns and target column as JSON fields.

Output shape

single item

The output item contains the trained model as a base64‑encoded pickle string in the model field, plus metadata such as model type, score, kernel, number of support vectors, classes (for classifiers), feature columns, and the number of training samples.

Examples

Example 1: Train a classifier on a CSV dataset

Set Model Type to Classifier (SVC), Feature Columns to sepal_length,sepal_width,petal_length,petal_width, Target Column to species, leave Kernel as RBF, C as 1, Gamma as Scale, enable Probability, and point Python Path to your Python environment with scikit‑learn.