HungaBunga

Visit Tool

HungaBunga is an AI Agents & Automation tool that automates the selection of scikit-learn models and their parameters. It brute-forces all available models and hyperparameters to find the best fit for your data.

Claim this tool

No Views Yet

At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is HungaBunga?

HungaBunga is a powerful tool designed to automate the often time-consuming process of model selection in machine learning. It systematically brute-forces all scikit-learn models, including both classification and regression types, along with their respective hyperparameters. By running through every possible combination and ranking them using cross-validation, HungaBunga identifies the optimal model for a given dataset. This automation significantly streamlines the machine learning workflow, allowing data scientists and developers to quickly discover high-performing models without extensive manual experimentation. It supports various model categories like Generalized Linear Models, SVMs, Nearest Neighbors, Trees, and Ensemble methods, making it a comprehensive solution for model optimization.

Best used for

Ideal for data scientists and developers who need to quickly identify the best machine learning model, optimize model performance, and automate the model selection process. Especially valuable for accelerating experimentation and ensuring comprehensive testing of scikit-learn models and their parameters.

Common actions

automate model selection

optimize machine learning

find best sklearn model

test hyperparameters

collaborationautomated workflowdeepfakeworkflowslow-code/no-codeopen-sourceface swappinggithub copilot"AI Agents"

Capabilities

Key features

Brute-force sklearn models
Test all hyperparameters
Rank models by cross-validation
Supports classification
Supports regression

Target Audience

data scientistdeveloper

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of scikit-learn models does HungaBunga support?

HungaBunga supports a wide range of scikit-learn models for both classification and regression tasks. This includes Generalized Linear Models, Kernel Ridge, Support Vector Machines, Nearest Neighbors, Gaussian Processes, Naive Bayes, Trees, Neural Networks, and Ensemble methods.

How does HungaBunga determine the best model?

HungaBunga evaluates models by running through all possible hyperparameters for each scikit-learn model. It then ranks these models based on their performance using cross-validation, providing a clear winner with its score.

What are the system requirements for installing HungaBunga?

To install HungaBunga, you need Python (>= 2.7), NumPy (>= 1.11.0), SciPy (>= 0.17.0), joblib (>= 0.11), scikit-learn (>=0.20.0), tabulate (>=0.8.2), and tqdm (>=4.28.1). It can be installed via pip.

Trending

Subcategories trending in AI Agents & Automation

AI Frameworks & Infra Chatbots & Conversational AI General-Purpose Agents Personal Assistants RAG & Document AI Voice Agents

Trending

Explore

Browse AI tools by category

Content & Design Productivity & Business Coding & Development AI Agents & Automation Research & Education Wellness & Lifestyle Career Development Marketing & Growth Data & Analytics Customer Support & CX Finance E-commerce