Search Results for "python feature selection"

...Real-time features are essential for many machine learning applications, such as real-time personalized recommendations and risk analytics. However, a feature engineering script developed by data scientists (Python scripts in most cases) cannot be directly deployed into production for online inference because it usually cannot meet the engineering requirements, such as low latency, high throughput and high availability.

CUDA-JMI is a parallel tool to accelerate the feature selection process using Joint Mutual Information as metric. This tool receives as input a file with ARFF, CVS or LIBSVM extensions that contais the values of m individuals and n features and returns a file with those features that provide more non-rendundant information.

xLearn is a high-performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM), all of which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning problems on large-scale sparse data. Many real-world datasets deal with high dimensional sparse feature vectors like a recommendation system where the number of categories and...

mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and of GSL. mlpy provides high-level functions and classes allowing, with few lines of code, the design of rich workflows for classification, regression, clustering and feature selection. mlpy is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License version 3.

CrfAny is a C++ package for efficient and exact training and inference of Conditional Random Fields over any graphical structure, supporting all feature types (boolean, integer and real) and command line, C++/Python Lib interfaces.