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Scikit Learn

Listed
davila7·vlatest·ai-ml·MIT

Scikit Learn enables rapid machine learning model building and experimentation using Python's popular library, boosting data analysis efficiency.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add davila7/claude-code-templates --skill scikit-learn -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

davila7

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Install target

Live repository

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About this skill

What it does

The scikit-learn skill provides tools for data mining and data analysis, including machine learning algorithms, preprocessing, model selection, and evaluation. It supports a wide range of supervised and unsupervised learning methods, making it suitable for tasks like classification, regression, clustering, and dimensionality reduction.

When to use it

  • To build predictive models using historical data for classification or regression problems.
  • For preprocessing datasets by handling missing values, scaling features, or encoding categorical variables.
  • When evaluating model performance with metrics such as accuracy, precision, recall, or F1 score.
  • To perform clustering analysis on unlabeled data to identify patterns or groupings.

Key capabilities

  • Supervised learning algorithms (e.g., SVM, Random Forests, Logistic Regression)
  • Unsupervised learning algorithms (e.g., K-means, PCA)
  • Data preprocessing and feature engineering tools
  • Model evaluation and selection techniques

Example prompts

  • "Train a random forest classifier on the Iris dataset and evaluate its accuracy."
  • "Perform principal component analysis (PCA) to reduce the dimensionality of this dataset."
  • "Split this dataset into training and testing sets with an 80/20 ratio."

Tips & gotchas

  • Ensure your data is properly preprocessed before applying machine learning algorithms.
  • Some algorithms may require numerical input, so categorical variables should be encoded appropriately.