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Average Ratings 0 Ratings

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features
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support

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Write a Review

Description

A premier solution for modeling, forecasting, and enhancing processes through multivariate statistical analysis and engaging visual representations. Accelerate product development, elevate quality, and refine processes by simplifying the analysis of extensive data sets more efficiently than previously possible. Achieve quicker and more precise solutions to practical challenges by examining diverse data types to enhance product creation, quality assurance, and manufacturing operations. Utilize advanced multivariate statistical analysis techniques, specifically designed for spectroscopy and chemometrics, to gain insights. Import material, sensor, process, and spectral data in various formats, complemented by easy-to-use features for plotting, preprocessing, and modeling spectral information. Ensure superior product classification to maintain consistent quality across offerings. Enhance productivity and simplify every stage of the analytical process with streamlined project-based workflows. Additionally, utilize compliance features such as digital signatures, password protection, and audit trails to adhere to regulations including 21 CFR Part 11 and EU Annex 11, ensuring your processes meet industry standards. This comprehensive tool empowers organizations to make informed decisions, fostering innovation and operational excellence throughout.

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 No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

GitHub No 
Python No 

Integrations

GitHub Yes 
Python Yes 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Aspen Technology

Founded

1981

Country

United States

Website

www.aspentech.com/en/products/msc/aspen-unscrambler

Vendor Details

Company Name

Axel ARONIO DE ROMBLAY

Founded

2017

Website

mlbox.readthedocs.io/en/latest/

Product Features

Statistical Analysis

Analytics Yes 
Association Discovery Yes 
Compliance Tracking Yes 
File Management No 
File Storage Yes 
Forecasting Yes 
Multivariate Analysis Yes 
Regression Analysis Yes 
Statistical Process Control Yes 
Statistical Simulation Yes 
Survival Analysis No 
Time Series No 
Visualization Yes 

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

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