How do I use Statistical ML (Python) on Zeplik?

Sign in to Zeplik and ask in plain language, or type /statistical-ml-python in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.

Which AI model does the Statistical ML (Python) skill use?

Any model you choose. Zeplik works across every model in one chat, so the Statistical ML (Python) skill runs on your preferred model for the task.

Where does the Statistical ML (Python) skill come from?

The Statistical ML (Python) skill is adapted from the open-source davila7/claude-code-templates project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.

How much does the Statistical ML (Python) skill cost?

Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.

Statistical ML (Python)

Data and analytics skill, available on Zeplik

Statistical ML (Python) is a ready-to-run data and analytics skill on Zeplik. Statistical and classical machine learning in Python — scikit-learn modeling, statsmodels inference, PyMC Bayesian modeling, SHAP explainability, UMAP, survival analysis and time-series. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.

The Statistical ML (Python) skill loads automatically when your request matches it, or you can invoke it directly by typing /statistical-ml-python in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.

What the Statistical ML (Python) skill can do

Fit and tune scikit-learn models for classification and regression tasks
Run statistical inference and hypothesis testing with statsmodels
Build Bayesian models and estimate posteriors using PyMC
Explain model predictions with SHAP values and generate feature importance plots

Try these prompts on Zeplik

Pick a prompt to open it in the Zeplik app. If you are not signed in yet, your prompt is waiting for you the moment you do.

How the Statistical ML (Python) skill works

/statistical-ml-python

Umbrella for statistical and classical ML in Python. The user has a dataset and a modeling or inference goal; pick the library (scikit-learn, statsmodels, PyMC, SHAP, UMAP, scikit-survival, aeon, pymoo) and deliver runnable analysis code plus interpretation. For deep learning route to the ai-ml training/serving skills.

Dispatch table

Pick the reference file(s) that match the request, read them, then answer. Read at most 2-3 files per turn.

Topic	Read
This skill should be used for time series machine learning tasks including classificati…	references/aeon.md
Bayesian modeling with PyMC.	references/pymc-bayesian-modeling.md
Multi-objective optimization framework.	references/pymoo.md
Machine learning in Python with scikit-learn.	references/scikit-learn.md
Comprehensive toolkit for survival analysis and time-to-event modeling in Python using…	references/scikit-survival.md
Model interpretability and explainability using SHAP (SHapley Additive exPlanations).	references/shap.md
Statistical modeling toolkit.	references/statsmodels.md
UMAP dimensionality reduction.	references/umap-learn.md

How to work

Identify which leaf topic the request maps to from the dispatch table above; establish the concrete inputs (language, dataset, framework, file format) and the goal. Ask for a missing detail rather than guessing.
Read the matching reference file(s) before answering. Read at most 2-3 per turn.
Deliver runnable artifacts — code, configs, specs — with a short rationale, matching the user's existing conventions when they paste code.
Confirm any decision the source flags (versions, thresholds, tradeoffs) with the user instead of guessing.

Usage

/statistical-ml-python $ARGUMENTS

How to use the Statistical ML (Python) skill

Sign in to Zeplik
Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the Statistical ML (Python) skill right away.
Describe your data and analytics task
Ask in plain language, or type /statistical-ml-python to invoke the skill directly. Zeplik recognizes the Statistical ML (Python) skill and applies its method.
Review and refine the result
Zeplik returns a clear, structured answer. Ask follow-ups in the same chat to refine it or take the next step.

Source and credit

Author: davila7 (D7 umbrella-consolidation)
License: MIT

Adapted from the open-source davila7/claude-code-templates project and tuned to run natively on Zeplik. View source on GitHub.

Frequently asked questions

What is the Statistical ML (Python) skill?: Statistical ML (Python) is a ready-to-run data and analytics skill on Zeplik. Statistical and classical machine learning in Python — scikit-learn modeling, statsmodels inference, PyMC Bayesian modeling, SHAP explainability, UMAP, survival analysis and time-series. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
How do I use Statistical ML (Python) on Zeplik?: Sign in to Zeplik and ask in plain language, or type /statistical-ml-python in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
Which AI model does the Statistical ML (Python) skill use?: Any model you choose. Zeplik works across every model in one chat, so the Statistical ML (Python) skill runs on your preferred model for the task.
Where does the Statistical ML (Python) skill come from?: The Statistical ML (Python) skill is adapted from the open-source davila7/claude-code-templates project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.
How much does the Statistical ML (Python) skill cost?: Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.

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