Do I need to write Python code manually?

No, Claude generates the required Python code and utilizes specialized plugins to execute the training and evaluation based on your natural language prompts.

What types of classification tasks can this skill handle?

It can handle any supervised learning task involving labeled data, including binary and multi-class classification problems.

Does it handle data preprocessing?

Yes, the skill automatically manages data cleaning, feature selection, and validation to ensure high-quality model inputs.

Which evaluation metrics are provided?

The skill generates reports featuring accuracy, precision, recall, and F1-scores to give a complete view of model performance.

Classification Model Builder

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

byintent-solutions-io

Data Science & ML

Automates the end-to-end process of building, training, and evaluating robust classification models for supervised learning tasks.

About

This skill empowers Claude to act as an automated data science assistant, facilitating the creation of classification models from raw datasets or specific user requirements. By leveraging the classification-model-builder plugin, it handles the entire machine learning pipeline—including data preprocessing, feature selection, hyperparameter optimization, and detailed performance reporting—allowing developers to quickly deploy reliable classifiers for tasks like spam detection, churn prediction, or sentiment analysis without manual coding.

Key Features

Direct integration with classification-model-builder plugins
Automated model selection and training pipelines
Smart data preprocessing and feature extraction
Hyperparameter optimization and tuning
0 GitHub stars
Comprehensive performance metrics (Accuracy, F1-score, Precision, Recall)

Use Cases

Automating the categorization of support tickets or documents
Building a spam detection system for email or messaging platforms
Predicting customer churn based on historical behavioral data

About

Key Features

Direct integration with classification-model-builder plugins
Automated model selection and training pipelines
Smart data preprocessing and feature extraction
Hyperparameter optimization and tuning
0 GitHub stars
Comprehensive performance metrics (Accuracy, F1-score, Precision, Recall)

Use Cases

Automating the categorization of support tickets or documents
Building a spam detection system for email or messaging platforms
Predicting customer churn based on historical behavioral data