Welcome to pytorch-tabnet2’s documentation!

Introduction

• Introduction
  • Installation
  • Original Repository
  • Key Features
  • Project Changes from the Original Implementation

Guides

• Basic Usage
  • Classification Example
  • Regression Example
  • Multi-task Classification Example
• Categorical Embedding
  • Categorical Embedding Example: Classification
  • Categorical Embedding Example: Regression
• Custom Metrics and Losses
  • Custom Evaluation Metric Example
  • Custom Loss Function Example
• Pretraining and Transfer Learning
  • Pretraining Example
  • Transfer Learning Example
• Data Augmentation and Callbacks
  • Data Augmentation Example
  • Custom Callback Example
• Model Saving and Loading
  • Saving and Loading a Classifier
  • Saving and Loading a Regressor

Models & Usage

• TabNet Pretrainer
  • Example
• TabNet Regressor
  • Example
• TabNet Classifier
  • Example
• TabNet Multi-Task Regressor
  • Example
• TabNet Multi-Task Classifier
  • Example

Metrics

• Classification Metrics
  • Accuracy
  • AUC (Area Under the ROC Curve)
  • Balanced Accuracy
  • Log Loss (Cross-Entropy Loss)
• Regression Metrics
  • Mean Absolute Error (MAE)
  • Mean Squared Error (MSE)
  • Root Mean Squared Error (RMSE)
  • Root Mean Squared Logarithmic Error (RMSLE)
• Unsupervised Metrics
  • Unsupervised Loss
  • Unsupervised Metrics
  • Base Metrics

Credits

• Credits
  • Original Repository

Indices and tables

• Index

• Module Index

• Search Page