aps2020
evalml
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aps2020 | evalml | |
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2 | 2 | |
17 | 712 | |
- | 2.9% | |
0.0 | 8.7 | |
almost 2 years ago | 4 days ago | |
R | Python | |
GNU General Public License v3.0 only | BSD 3-clause "New" or "Revised" License |
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aps2020
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[R] Feature selection algorithms for psychometric questions when the predicted variable is multi-dimensional?
try Copula Entropy for feature/variable selection. see my paper "Variable Selection with Copula Entropy". The code for the paper is at here, in which the R package 'copent' is used.
- [Q] Feature selection (>600 features)
evalml
What are some alternatives?
vip - Variable Importance Plots (VIPs)
Sklearn-genetic-opt - ML hyperparameters tuning and features selection, using evolutionary algorithms.
dcor - Distance correlation and related E-statistics in Python
easyopt - zero-code hyperparameters optimization framework
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
SAP-HANA-AutoML - Python Automated Machine Learning library for tabular data.
Auto_ViML - Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
powershap - A power-full Shapley feature selection method.
llm_optimize - LLM Optimize is a proof-of-concept library for doing LLM (large language model) guided blackbox optimization.
FeatureHub - The most comprehensive library of AI/ML features across multiple domains. Our goal is to create a dataset that serves as a valuable resource for researchers and data scientists worldwide