Does it support hyperparameter tuning?

The skill configures optimal default training parameters, but you can also request specific experiments with different settings to improve performance.

How do I use this skill with my own data?

Simply provide a dataset or describe the data location in your prompt, and use keywords like 'train' or 'evaluate' to trigger the automated workflow.

Can it handle both classification and regression tasks?

Yes, the skill automatically analyzes the dataset and target variable to determine whether a classification or regression model is most appropriate.

What frameworks does this skill support?

This skill primarily supports common machine learning frameworks like scikit-learn for training and evaluating models.

Does it provide performance metrics after training?

Absolutely. It generates key metrics such as accuracy, precision, recall, Mean Squared Error (MSE), and R-squared depending on the model type.

Machine Learning Model Trainer

Name: Machine Learning Model Trainer
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Automates the end-to-end process of training, evaluating, and persisting machine learning models from provided datasets.

About

The Machine Learning Model Trainer skill empowers Claude to function as an automated data science assistant, streamlining the model development lifecycle. It intelligently analyzes datasets to identify target variables, selects the most appropriate algorithms for classification or regression tasks, and executes training using robust cross-validation techniques. By generating comprehensive performance metrics and saving the resulting model artifacts, this skill simplifies the transition from raw data to actionable predictive models, making it ideal for rapid prototyping and automated ML workflows.

Key Features

3 GitHub stars
Persistence of trained model artifacts for future use
Automated data analysis and target variable identification
Detailed performance reporting (Accuracy, Precision, MSE, R-squared)
Built-in cross-validation and training parameter configuration
Intelligent model selection for classification and regression

Use Cases

Automating the evaluation of multiple scikit-learn algorithms on a new dataset
Building a regression model to forecast house prices based on property features
Training a classification model to predict customer churn from historical data

About

Key Features

3 GitHub stars
Persistence of trained model artifacts for future use
Automated data analysis and target variable identification
Detailed performance reporting (Accuracy, Precision, MSE, R-squared)
Built-in cross-validation and training parameter configuration
Intelligent model selection for classification and regression

Use Cases

Automating the evaluation of multiple scikit-learn algorithms on a new dataset
Building a regression model to forecast house prices based on property features
Training a classification model to predict customer churn from historical data