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LAMDA TALENT
LAMDA-TALENT
L
LAMDA TALENT
AI Data MiningAI Development Assistant#Tabular Data#Deep Learning#Machine Learning#Data Science#BenchmarkingStandard PicksOpen Source
Overview :
LAMDA-TALENT is a comprehensive tabular data analysis toolbox and benchmarking platform that integrates over 20 deep learning methods, 10 traditional methods, and 300+ diverse tabular datasets. This toolbox aims to enhance model performance on tabular data, offers robust preprocessing capabilities, optimizes data learning, and supports user-friendly and adaptable operations suitable for both novice and expert data scientists.
Target Users :
LAMDA-TALENT is primarily targeted towards data scientists, machine learning researchers, and developers, especially those who work with large volumes of tabular data. It provides a powerful platform to help users improve model performance and optimize data processing workflows using a variety of deep learning and traditional methods.
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Top Region： US(19.34%)
Website Views ： 48.0K
Use Cases
Use LAMDA-TALENT for preprocessing tabular data and training models.
Leverage the integrated datasets and methods to analyze and predict specific business problems.
Compare the performance of different models using the visualization tools to select the optimal solution.
Features
Integrates 20+ deep learning architectures, including MLP, ResNet, SNN, and more.
Includes 300 datasets covering various task types, size distributions, and data domains.
Supports customizable addition of datasets and methods, offering high flexibility.
Supports diverse standardization, encoding, and measurement methods.
Provides fair and comprehensive evaluation, including accuracy and root mean squared error (RMSE) for classification and regression tasks.
Visualization tools that help researchers and practitioners quickly and fairly assess the pros and cons of different methods.
How to Use
Clone the GitHub repository to your local environment.
Edit the configuration file to set global parameters and hyperparameters.
Run the training script for deep learning methods or traditional methods.
Add new methods or datasets to the toolbox as needed.
Use the provided tools to evaluate and compare model performance.
Select the model best suited for your specific task based on the evaluation results.
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