---

name: classification-regression description: "Train, evaluate, serialize, and apply pyAudioAnalysis sklearn-backed audio classifiers and regressors." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

classification-regression

Use this sub-skill when the task is to train, evaluate, serialize, load, or apply pyAudioAnalysis audio classifiers or regression models backed by scikit-learn-style estimators and the package's in-module kNN wrapper.

Read first

• references/api-reference.md for exact Python API signatures, supported model identifiers, return values, and side effects.
• references/workflows.md for end-to-end classifier and regression workflows that keep model outputs isolated.
• references/model-artifacts.md before moving, reusing, or deleting trained model files.
• references/troubleshooting.md when training produces no model, inference returns -1, SMOTE fails, model types disagree, or serialized models behave differently across environments.
• scripts/classification_smoke.py --help for a bounded synthetic classifier smoke test.

Owns

• Folder-organized supervised audio classification: one class per input folder.
• Classifier training through audioTrainTest.extract_features_and_train(...).
• File and folder classification through audioTrainTest.file_classification(...) or a small loop around it.
• Held-out folder evaluation through audioTrainTest.evaluate_model_for_folders(...).
• Audio regression training through audioTrainTest.feature_extraction_train_regression(...) and inference through audioTrainTest.file_regression(...).
• Model artifact naming, compatibility, and cleanup for these classifiers/regressors.

Route elsewhere

• Raw short-term or mid-term feature matrix generation belongs to the feature-extraction sub-skill.
• Time segmentation, HMMs, diarization, and segment-level classification belong to the segmentation-diarization sub-skill.
• Full command-line syntax catalogs and legacy script execution details belong to the cli-and-io sub-skill.
• Visualization-only tasks belong outside this sub-skill except for the optional plots produced by classifier evaluation.

Operating defaults

Prefer CPU-only package APIs:

from pyAudioAnalysis import audioTrainTest as aT

Keep trained model prefixes under an explicit project output directory, use unique model names per experiment, set plot=False in headless evaluation, and treat pickle model files as trusted-environment artifacts only.