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name: analytics description: "Guides SecretFlow preprocessing, statistics, and classical ML workflows that use the direct Python APIs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Analytics

Use this sub-skill when the task is about direct SecretFlow preprocessing, statistics, or classical ML APIs rather than the component CLI.

Owns

• preprocessing helpers such as StandardScaler
• statistics helpers such as psi_eval, table statistics, and score-card flows
• direct ML classes in secretflow.ml.linear, secretflow.ml.cluster, secretflow.ml.naive_bayes, secretflow.ml.gaussian_process, and secretflow.ml.neighbors
• training / prediction workflows that use the direct Python APIs
• common optional-dependency and SPU configuration failures

Does not own

• component CLI / component export workflows — use component-cli
• device/object setup and federated containers — use runtime-data
• PSI deployment, Kuscia, or TEEU orchestration — use privacy-orchestration

Trigger phrases

Use this route when a user asks things like:

• how to scale or preprocess federated data
• how to compute PSI or score-card metrics directly
• how to fit SecretFlow's direct ML estimators
• how to compare SecretFlow's model APIs with scikit-learn
• why a model needs SPU, HEU, or a particular party layout

Reading order

1. Read references/analytics.md for the algorithm map and workflow choices.
2. Read the root troubleshooting page if the import or optional dependency path is broken.
3. Use scripts/analytics_smoke.py when you need a tiny import-only proof that the analytics surface is available.

Workflow

1. Decide whether the user wants a data transform, a metric, or a model.
2. Identify the required party/device layout before choosing the class or metric.
3. For preprocessing and statistics, confirm the dataframe shape and the owning parties before you fit or evaluate anything.
4. For ML classes, start with the direct Python constructor and only escalate to a component-based path if the user specifically needs export or CLI-driven execution.

Common decisions

• Use StandardScaler for federated tabular normalization.
• Use psi_eval for the PSI-style score calculations already built into the direct API.
• Use the secretflow.ml.linear classes for logistic-regression and GLM-style tasks.
• Use KMeans, GNB, GPC, or KNNClassifer when the task names a classical estimator family directly.

Bundled files

• references/analytics.md — algorithm catalog, input expectations, and troubleshooting.
• scripts/analytics_smoke.py — small import and signature smoke helper.