Does it handle data cleaning?

Yes, the skill automates best practices for data validation and preprocessing to ensure the input data is ready for model training.

What types of models can this skill build?

This skill focuses on classification models, which are used to predict categorical outcomes like 'spam vs. not spam' or 'high vs. low risk'.

Which performance metrics are provided?

The skill reports essential classification metrics including accuracy, precision, recall, and F1-score to help you evaluate model quality.

How do I activate this skill in Claude Code?

Simply ask Claude to 'build a classifier', 'train a classification model', or provide a dataset with a specific target variable to predict.

Can it help with model deployment?

Yes, by leveraging the underlying plugin, it can generate code for FastAPI services to help you deploy your model as a functional API.

Classification Model Builder

Name: Classification Model Builder
Author: intent-solutions-io

byintent-solutions-io

Ciencia de Datos y ML

Automates the end-to-end creation, evaluation, and deployment of machine learning classification models from natural language requests.

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The Classification Model Builder skill streamlines the machine learning workflow by enabling Claude to automatically construct supervised learning pipelines. It handles the complexities of data preprocessing, feature selection, and algorithm optimization to generate robust classification models. Whether you are building a spam filter or a churn prediction engine, this skill ensures best practices in data validation and performance reporting, providing ready-to-use code and insights to accelerate your data science projects.

Características Principales

Comprehensive performance reporting (Accuracy, Precision, Recall, F1)
0 GitHub stars
Automated data preprocessing and feature engineering
Intelligent model selection and hyperparameter tuning
Seamless integration with existing datasets and specifications
Generation of production-ready FastAPI service code

Casos de Uso

Categorizing large volumes of text or documents into labeled classes
Predicting customer churn and behavior based on historical data
Developing automated spam and fraud detection systems

Acerca de

Características Principales

Comprehensive performance reporting (Accuracy, Precision, Recall, F1)
0 GitHub stars
Automated data preprocessing and feature engineering
Intelligent model selection and hyperparameter tuning
Seamless integration with existing datasets and specifications
Generation of production-ready FastAPI service code

Casos de Uso

Categorizing large volumes of text or documents into labeled classes
Predicting customer churn and behavior based on historical data
Developing automated spam and fraud detection systems