Initial commit

skills/classification-model-builder/SKILL.md

@@ -0,0 +1,52 @@
+---
+name: building-classification-models
+description: |
+  This skill enables Claude to construct and evaluate classification models using provided datasets or specifications. It leverages the classification-model-builder plugin to automate model creation, optimization, and reporting. Use this skill when the user requests to "build a classifier", "create a classification model", "train a classification model", or needs help with supervised learning tasks involving labeled data. The skill ensures best practices are followed, including data validation, error handling, and performance metric reporting.
+allowed-tools: Read, Write, Edit, Grep, Glob, Bash
+version: 1.0.0
+---
+
+## Overview
+
+This skill empowers Claude to efficiently build and deploy classification models. It automates the process of model selection, training, and evaluation, providing users with a robust and reliable classification solution. The skill also provides insights into model performance and suggests potential improvements.
+
+## How It Works
+
+1. **Context Analysis**: Claude analyzes the user's request, identifying the dataset, target variable, and any specific requirements for the classification model.
+2. **Model Generation**: The skill utilizes the classification-model-builder plugin to generate code for training a classification model based on the identified dataset and requirements. This includes data preprocessing, feature selection, model selection, and hyperparameter tuning.
+3. **Evaluation and Reporting**: The generated model is trained and evaluated using appropriate metrics (e.g., accuracy, precision, recall, F1-score). Performance metrics and insights are then provided to the user.
+
+## When to Use This Skill
+
+This skill activates when you need to:
+- Build a classification model from a given dataset.
+- Train a classifier to predict categorical outcomes.
+- Evaluate the performance of a classification model.
+
+## Examples
+
+### Example 1: Building a Spam Classifier
+
+User request: "Build a classifier to detect spam emails using this dataset."
+
+The skill will:
+1. Analyze the provided email dataset to identify features and the target variable (spam/not spam).
+2. Generate Python code using the classification-model-builder plugin to train a spam classification model, including data cleaning, feature extraction, and model selection.
+
+### Example 2: Predicting Customer Churn
+
+User request: "Create a classification model to predict customer churn using customer data."
+
+The skill will:
+1. Analyze the customer data to identify relevant features and the churn status.
+2. Generate code to build a classification model for churn prediction, including data validation, model training, and performance reporting.
+
+## Best Practices
+
+- **Data Quality**: Ensure the input data is clean and preprocessed before training the model.
+- **Model Selection**: Choose the appropriate classification algorithm based on the characteristics of the data and the specific requirements of the task.
+- **Hyperparameter Tuning**: Optimize the model's hyperparameters to achieve the best possible performance.
+
+## Integration
+
+This skill integrates with the classification-model-builder plugin to automate the model building process. It can also be used in conjunction with other plugins for data analysis and visualization.