automl-optimizer

What This Skill Does

The automl-optimizer is a sophisticated OpenClaw agent skill designed to bridge the gap between raw data and high-performing machine learning models. Instead of relying on guesswork or exhaustive manual labor to tune hyperparameters, this skill leverages industry-standard libraries like Optuna to conduct systematic searches of parameter spaces. It utilizes advanced strategies such as Bayesian optimization to find the most effective model configurations, pruning underperforming paths early to save compute resources while ensuring that no viable candidate is left unexplored.

Installation

To integrate this skill into your environment, run the following command in your terminal: clawhub install openclaw/skills/skills/anton-abyzov/sw-automl-optimizer Ensure your project has the required dependencies installed to support the backend optimization engines.

Use Cases

• Production Model Tuning: Perfect for refining gradient boosting machines (XGBoost, LightGBM) for high-stakes business metrics.
• Neural Architecture Search: Automatically finding the optimal layers, dropout rates, and activation functions for deep learning models.
• Baseline Benchmarking: Quickly identifying the best-performing algorithm among a set of candidates for a new dataset.
• Reproducible ML Pipelines: Generating comprehensive reports that document exactly how an optimal configuration was reached, satisfying audit and transparency requirements.

Example Prompts

1. "OpenClaw, please run an automl sweep on my current XGBoost project; focus on learning rate and depth to maximize AUC."
2. "I need to optimize the hyperparameters for my random forest model. Use Optuna to run 50 trials and output the best configuration."
3. "Can you perform a neural architecture search for my image classification task? I'm looking for the most efficient model structure."

Tips & Limitations

• Resource Management: Hyperparameter tuning can be compute-intensive. Start with a smaller budget of trials (e.g., 20-50) before committing to hundreds of iterations.
• Search Space Definition: Be precise with your search ranges. Overly broad ranges can lead to slower convergence, while ranges that are too narrow might miss global optima.
• Data Integrity: Ensure your cross-validation strategy is robust to prevent overfitting to your validation set during the tuning process.
• Scope: While excellent for tuning, this skill assumes the data cleaning and feature engineering steps are already finalized. It optimizes models, not data quality.