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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

BioVinci streamlines the process of applying advanced visualization techniques to your high-dimensional data by automatically executing cutting-edge methods and suggesting the most effective one. Users can delve into high-dimensional datasets utilizing various machine learning approaches, including dimensionality reduction and feature selection. Transform extensive datasets into informative graphics effortlessly, without the need for coding skills. The platform provides a variety of graph types and customization options to effectively showcase research findings. It empowers scientists with little to no programming background to efficiently apply top-tier machine learning strategies to their data and produce elegant visualizations that uncover valuable insights that might otherwise remain hidden. We particularly emphasize the user-friendly design of BioVinci 2.0, ensuring that even those encountering it for the first time can navigate it with ease. With an extensive array of plot configurations tailored to meet diverse user requirements, our goal is to deliver visuals that are not only aesthetically pleasing and simple but also interactive, publication-ready, and rich in information. Additionally, we believe that enhancing the usability of our software will foster greater engagement and facilitate deeper understanding among researchers.

Description

MLBox is an advanced Python library designed for Automated Machine Learning. This library offers a variety of features, including rapid data reading, efficient distributed preprocessing, comprehensive data cleaning, robust feature selection, and effective leak detection. It excels in hyper-parameter optimization within high-dimensional spaces and includes cutting-edge predictive models for both classification and regression tasks, such as Deep Learning, Stacking, and LightGBM, along with model interpretation for predictions. The core MLBox package is divided into three sub-packages: preprocessing, optimization, and prediction. Each sub-package serves a specific purpose: the preprocessing module focuses on data reading and preparation, the optimization module tests and fine-tunes various learners, and the prediction module handles target predictions on test datasets, ensuring a streamlined workflow for machine learning practitioners. Overall, MLBox simplifies the machine learning process, making it accessible and efficient for users.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

GitHub
Python

Integrations

GitHub
Python

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

BioVinci

Country

United States

Website

vinci.bioturing.com/feature

Vendor Details

Company Name

Axel ARONIO DE ROMBLAY

Founded

2017

Website

mlbox.readthedocs.io/en/latest/

Product Features

Data Visualization

Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery

Product Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

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