What is Tabular Regression in AI?

Tabular regression in AI is a machine learning technique that focuses on predicting continuous numerical outcomes from structured tabular data. In contrast to classification, which predicts categorical labels, regression aims to predict quantitative values, making it essential for applications like price estimation, risk assessment, and forecasting. Tabular regression is widely used across sectors where structured data in rows and columns is abundant, such as in finance, healthcare, and engineering.

Tabular Regression
Figure 1 - Tabular Regression

Where can you find AI Tabular Regression models

This is the link to use to filter Hunggingface models for Tabular Regression:

https://huggingface.co/models?pipeline_tag=tabular-regression&sort=trending

Our favourite Model Authors:

The most interesting Tabular regression project

One of the most interesting Tabular regression projects is called My Temperature Conversion Model.

This model is a simple neural network that converts temperatures from Celsius to Fahrenheit.

Model Description

This model was created as a practice exercise for the course "Intro to TensorFlow for Deep Learning" from Udacity, given by TensorFlow. It was trained on a dataset of temperature values in Celsius and their corresponding values in Fahrenheit. The model uses a small neural network built with TensorFlow.

https://huggingface.co/prabinpanta0/celsius-to-fahrenheit

Definition of Tabular Regression in AI

Tabular regression is a supervised machine learning method that models the relationship between input features (independent variables) in tabular data and a continuous target variable (dependent variable). The model is trained on historical data to learn patterns, enabling it to predict future values for new data entries.

Examples of Tabular Regression

  • Real Estate Price Prediction: Estimates property prices based on features such as location, square footage, and amenities.
  • Stock Market Forecasting: Predicts future stock prices by analyzing historical price trends, volume, and other economic indicators.
  • Sales Forecasting: Predicts future sales volume based on historical sales data, seasonal trends, and marketing factors.
  • Weather Prediction: Estimates temperature or rainfall levels based on historical meteorological data.
  • Health Risk Assessment: Predicts health risks, such as the likelihood of a disease based on factors like age, weight, and lifestyle.

How Tabular Regression Works

In tabular regression, the dataset consists of rows representing individual data points and columns representing features. A target variable is used as the output that the model attempts to predict. The model is trained to find the best fit between features and the target by minimizing errors on training data, allowing it to predict continuous values for new, unseen data points.

Steps to Implement Tabular Regression

Step 1: Data Preparation

Clean and preprocess the data by handling missing values, encoding categorical variables, scaling numerical values, and splitting the data into training and testing sets.

Step 2: Feature Selection

Choose the most influential features to improve model accuracy. Methods such as correlation analysis and feature importance ranking can help.

Step 3: Select a Regression Model

Choose an appropriate regression algorithm. Common algorithms for tabular regression include Linear Regression, Decision Trees, Random Forest, Gradient Boosting Machines (GBM), and Neural Networks.

Step 4: Model Training

Train the model on the training dataset, allowing it to learn the relationship between features and the target variable by minimizing errors.

Step 5: Model Evaluation

Evaluate the model on test data using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared to gauge model accuracy.

Step 6: Deployment and Monitoring

Deploy the trained model for production use, and monitor its performance over time to ensure it maintains accuracy as new data arrives.

Applications of Tabular Regression

  • Finance: Used in portfolio optimization, credit scoring, and risk assessment to predict continuous variables such as interest rates, returns, and loan defaults.
  • Healthcare: Helps in predicting patient outcomes, determining treatment efficacy, and estimating healthcare costs.
  • Manufacturing: Used for predictive maintenance, yield prediction, and quality control to improve production efficiency.
  • Retail: Sales and demand forecasting, pricing optimization, and customer lifetime value prediction are common applications.
  • Environmental Science: Climate modeling and pollution level forecasting benefit from tabular regression models for improved decision-making.

Popular Algorithms for Tabular Regression

Linear Regression: Models the linear relationship between features and target variables, commonly used for straightforward regression tasks.

Decision Trees: Use a tree-like structure to partition data based on feature values. While interpretable, they can be prone to overfitting.

Random Forest: An ensemble of decision trees that reduces overfitting by averaging multiple decision trees' predictions.

Gradient Boosting Machines (GBM): Sequentially improves predictions by focusing on errors from previous models, as seen in XGBoost and LightGBM.

Neural Networks: Used for complex relationships in tabular data, although computationally more intensive and harder to interpret.

Challenges in Tabular Regression

  • Data Quality: Missing or noisy data can impact model accuracy, making data cleaning and preprocessing essential.
  • Feature Engineering: Transforming raw data into meaningful features can significantly impact performance.
  • Model Interpretability: Complex models, like neural networks, are harder to interpret, which can limit their use in fields requiring high transparency.
  • Overfitting: Overfitting is a common issue in regression, where models perform well on training data but poorly on new data.
  • Scalability: Large datasets may demand significant computational power, making it crucial to select efficient algorithms and optimize code.

Benefits of Tabular Regression

  • Accurate Prediction: Provides precise continuous value predictions, essential for decision-making in finance, healthcare, and other industries.
  • Flexible Models: Various algorithms can model both simple and complex relationships, from linear regression to deep neural networks.
  • Data Utilization: Makes full use of tabular data stored in databases and spreadsheets, allowing for analysis of existing data sources.
  • Improved Decision-Making: Accurate regression predictions enable better planning and decision-making across sectors.

Future of Tabular Regression

The future of tabular regression involves integrating deep learning with structured data to improve predictive performance, especially on complex datasets. Additionally, explainability tools and frameworks, like SHAP (SHapley Additive exPlanations), are advancing, allowing complex regression models to be interpretable and reliable in sensitive fields like healthcare and finance.

Further Resources for Learning Tabular Regression

  • Scikit-Learn Documentation - A comprehensive guide to implementing regression models in Python.
  • TensorFlow Guide - Learn to create neural networks for regression tasks using TensorFlow.
  • Kaggle - Offers datasets and projects to practice tabular regression tasks.
  • XGBoost Documentation - Learn about gradient boosting techniques for regression models.
  • Fast.ai - Practical courses and guides for machine learning and regression tasks.

Conclusion

Tabular regression in AI is a foundational approach to predicting continuous values in structured data. With applications spanning finance, healthcare, retail, and beyond, it empowers businesses and researchers to make informed decisions based on accurate predictions. A thorough understanding of data preprocessing, model selection, and evaluation metrics is crucial for building effective regression models that meet real-world demands.

How to setup a Tabular regression LLM on Ubuntu Linux

If you are ready to setup your first Tabular regression system follow the instructions in our next page:

How to setup a Tabular regression system

Image sources

Figure 1: https://medium.com/@okanyenigun/exploring-hugging-face-tabular-regression-44486ffee4cf

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