Can I use it for deep learning?

Yes, it supports Neural Architecture Search (NAS) to optimize layer counts, sizes, activations, and dropout rates in deep learning models.

What ML libraries are supported by this skill?

The skill integrates with popular frameworks like Optuna, Hyperopt, Scikit-Learn, XGBoost, LightGBM, and various other AutoML libraries.

Does it help compare different algorithms?

Absolutely. The AutoMLPipeline allows you to test multiple algorithms (like XGBoost vs. Random Forest) simultaneously to find the best performing model type.

How does it handle experiment logging?

It automatically records every trial, including parameters and metrics, into the .specweave directory for full reproducibility and documentation.

AutoML Optimizer

Name: AutoML Optimizer
Author: anton-abyzov

byanton-abyzov

Data Science & ML

Automates hyperparameter tuning and model selection using intelligent search strategies like Bayesian optimization.

About

AutoML Optimizer streamlines the machine learning development lifecycle by automating the complex task of hyperparameter tuning and algorithm selection. It leverages advanced frameworks like Optuna and Auto-sklearn to systematically explore model architectures and parameter spaces, replacing manual trial-and-error with data-driven optimization. By integrating directly with the SpecWeave ecosystem, it automatically tracks every experiment, generates comprehensive performance reports, and identifies the most robust configurations for production deployment.

Key Features

0 GitHub stars
Seamless integration with Optuna, Scikit-Learn, and XGBoost
Support for Neural Architecture Search (NAS) and multi-objective goals
Multi-algorithm benchmarking and automatic model selection
Automated experiment tracking and documentation via SpecWeave
Intelligent hyperparameter optimization via Bayesian sampling

Use Cases

Automating architecture search for deep learning neural networks
Optimizing gradient boosting models for tabular data performance
Benchmarking multiple ML frameworks to find the winning model

About

Key Features

0 GitHub stars
Seamless integration with Optuna, Scikit-Learn, and XGBoost
Support for Neural Architecture Search (NAS) and multi-objective goals
Multi-algorithm benchmarking and automatic model selection
Automated experiment tracking and documentation via SpecWeave
Intelligent hyperparameter optimization via Bayesian sampling

Use Cases

Automating architecture search for deep learning neural networks
Optimizing gradient boosting models for tabular data performance
Benchmarking multiple ML frameworks to find the winning model