52 lines
3.2 KiB
Markdown
52 lines
3.2 KiB
Markdown
---
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name: building-classification-models
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description: |
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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.
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allowed-tools: Read, Write, Edit, Grep, Glob, Bash
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version: 1.0.0
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---
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## Overview
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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.
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## How It Works
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1. **Context Analysis**: Claude analyzes the user's request, identifying the dataset, target variable, and any specific requirements for the classification model.
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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.
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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.
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## When to Use This Skill
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This skill activates when you need to:
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- Build a classification model from a given dataset.
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- Train a classifier to predict categorical outcomes.
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- Evaluate the performance of a classification model.
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## Examples
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### Example 1: Building a Spam Classifier
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User request: "Build a classifier to detect spam emails using this dataset."
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The skill will:
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1. Analyze the provided email dataset to identify features and the target variable (spam/not spam).
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2. Generate Python code using the classification-model-builder plugin to train a spam classification model, including data cleaning, feature extraction, and model selection.
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### Example 2: Predicting Customer Churn
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User request: "Create a classification model to predict customer churn using customer data."
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The skill will:
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1. Analyze the customer data to identify relevant features and the churn status.
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2. Generate code to build a classification model for churn prediction, including data validation, model training, and performance reporting.
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## Best Practices
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- **Data Quality**: Ensure the input data is clean and preprocessed before training the model.
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- **Model Selection**: Choose the appropriate classification algorithm based on the characteristics of the data and the specific requirements of the task.
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- **Hyperparameter Tuning**: Optimize the model's hyperparameters to achieve the best possible performance.
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## Integration
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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. |