Building Classification Models
- 40 installs
- 2.6k repo stars
- Updated August 5, 2026
- jeremylongshore/claude-code-plugins-plus-skills
Builds and evaluates supervised classification models from labeled data, covering preprocessing, model selection, tuning, and metrics.
About
Automates building classification models including data preprocessing, feature and model selection, hyperparameter tuning, and evaluation. A developer uses it to train and assess a classifier for categorical prediction tasks.
- Automated model selection and hyperparameter tuning
- Evaluation with accuracy, precision, recall, and F1
Building Classification Models by the numbers
- 40 all-time installs (skills.sh)
- +1 installs in the week ending Aug 2, 2026 (Skillselion tracking)
- Ranked #1,000 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 40 |
|---|---|
| repo stars | ★ 2.6k |
| Last updated | August 5, 2026 |
| Repository | jeremylongshore/claude-code-plugins-plus-skills ↗ |
What it does
Builds and evaluates supervised classification models from labeled data, covering preprocessing, model selection, tuning, and metrics.
Files
Classification Model Builder
Build and evaluate classification models for supervised learning tasks with labeled data.
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.
Prerequisites
- Appropriate file access permissions
- Required dependencies installed
Instructions
1. Invoke this skill when the trigger conditions are met 2. Provide necessary context and parameters 3. Review the generated output 4. Apply modifications as needed
Output
The skill produces structured output relevant to the task.
Error Handling
- Invalid input: Prompts for correction
- Missing dependencies: Lists required components
- Permission errors: Suggests remediation steps
Resources
- Project documentation
- Related skills and commands
{
"_comment": "Model configuration template for the classification model builder plugin.",
"model_name": "ExampleClassifier",
"_comment": "A descriptive name for your model.",
"model_type": "RandomForestClassifier",
"_comment": "The type of classification model to use (e.g., RandomForestClassifier, LogisticRegression, SVM).",
"data_path": "data/training_data.csv",
"_comment": "Path to the CSV file containing the training data.",
"target_column": "target",
"_comment": "The name of the column containing the target variable.",
"features": [
"feature1",
"feature2",
"feature3",
"feature4"
],
"_comment": "List of column names to use as features. If empty, all columns except the target_column will be used.",
"hyperparameters": {
"_comment": "Hyperparameters specific to the chosen model type.",
"n_estimators": 100,
"_comment": "Number of trees in the random forest (example for RandomForestClassifier).",
"max_depth": 10,
"_comment": "Maximum depth of the trees (example for RandomForestClassifier).",
"learning_rate": 0.1,
"_comment": "Learning rate for gradient boosting models (example for GradientBoostingClassifier)."
},
"training_parameters": {
"_comment": "Parameters related to the training process.",
"test_size": 0.2,
"_comment": "The proportion of the data to use for testing.",
"random_state": 42,
"_comment": "A random seed for reproducibility.",
"stratify": true,
"_comment": "Whether to stratify the target variable during train/test split."
},
"evaluation_metrics": [
"accuracy",
"precision",
"recall",
"f1-score",
"roc_auc"
],
"_comment": "List of evaluation metrics to compute on the test set.",
"model_save_path": "models/example_classifier.pkl",
"_comment": "Path to save the trained model.",
"feature_importance": true,
"_comment": "Boolean value to toggle feature importance calculation.",
"preprocessing": {
"_comment": "Configuration for data preprocessing steps.",
"handle_missing_values": "impute",
"_comment": "How to handle missing values (e.g., 'impute', 'remove', 'none').",
"missing_value_strategy": "mean",
"_comment": "Strategy for imputation (e.g., 'mean', 'median', 'most_frequent').",
"scale_features": true,
"_comment": "Whether to scale numerical features using StandardScaler or similar.",
"feature_scaling_method": "standard",
"_comment": "Method to use for feature scaling ('standard' or 'minmax')."
}
}Assets
Bundled resources for classification-model-builder skill
- [ ] model_config_template.json: A template JSON file for specifying model configurations, including hyperparameters and training parameters.
- [ ] example_dataset.csv: A sample CSV dataset that can be used for testing the classification model builder.
- [ ] report_template.html: An HTML template for generating the model performance report.
References
Bundled resources for classification-model-builder skill
Scripts
Bundled resources for classification-model-builder skill
- [x] model_builder.py: Automates the process of building, training, and evaluating classification models. Takes dataset path and model configuration as input.
- [x] data_validator.py: Validates the input dataset for common issues like missing values, incorrect data types, and imbalanced classes.
- [x] report_generator.py: Generates a comprehensive report of the model's performance, including metrics like accuracy, precision, recall, and F1-score.
Auto-Generated
Scripts generated on 2025-12-10 03:48:17
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