Can this skill help with feature engineering?

Yes, it includes specialized guidance for scaling, normalization, categorical encoding, and handling missing values using standard scikit-learn transformers.

Does it support hyperparameter optimization?

Absolutely. The skill provides patterns for GridSearchCV, RandomizedSearchCV, and successive halving to find the best possible parameters for your models.

Is this suitable for deep learning tasks?

This skill focuses on classical, interpretable machine learning. While scikit-learn has basic neural network support (MLP), it is primarily used for algorithms like Random Forests, SVMs, and Gradient Boosting.

What is the Scikit-Learn Claude Code Skill?

It is a specialized capability that provides Claude with domain-specific knowledge to assist in building classical machine learning models, from data cleaning to model deployment.

How do I install the required libraries for this skill?

You can quickly set up your environment by running 'uv pip install scikit-learn pandas numpy' as recommended in the skill documentation.

Scikit-Learn ML Framework

Name: Scikit-Learn ML Framework
Author: K-Dense-AI

byK-Dense-AI

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3,719

データサイエンスとML

Implements professional machine learning workflows in Python including classification, regression, clustering, and data preprocessing.

概要

This skill empowers Claude to architect and implement production-ready machine learning solutions using scikit-learn, the industry-standard Python library. It provides expert guidance on algorithm selection, data transformation, model evaluation, and the creation of robust ML pipelines, ensuring that your data science projects follow best practices for reproducibility, performance, and scalability while avoiding common pitfalls like data leakage.

主な機能

3,719 GitHub stars
Robust data preprocessing, scaling, and feature engineering techniques
Advanced model evaluation via cross-validation and grid search
Comprehensive supervised and unsupervised algorithm implementations
End-to-end ML pipeline construction using ColumnTransformers and Pipelines
Automated hyperparameter tuning and model selection strategies

ユースケース

Developing reproducible data science workflows for production environments
Building predictive models for classification and regression tasks
Performing customer segmentation and anomaly detection via clustering

概要

主な機能

3,719 GitHub stars
Robust data preprocessing, scaling, and feature engineering techniques
Advanced model evaluation via cross-validation and grid search
Comprehensive supervised and unsupervised algorithm implementations
End-to-end ML pipeline construction using ColumnTransformers and Pipelines
Automated hyperparameter tuning and model selection strategies

ユースケース

Developing reproducible data science workflows for production environments
Building predictive models for classification and regression tasks
Performing customer segmentation and anomaly detection via clustering