Table of Contents

Regression Tutorial

Learn how to predict continuous values using AiDotNet's regression algorithms.

Overview

Regression is a supervised learning technique used to predict continuous numerical values. AiDotNet provides 41+ regression algorithms to choose from.

Available Algorithms

Algorithm Use Case
Linear Regression Simple linear relationships
Ridge Regression Linear with L2 regularization
Lasso Regression Linear with L1 regularization (feature selection)
ElasticNet Combined L1/L2 regularization
Polynomial Regression Non-linear relationships
Support Vector Regression Complex non-linear patterns
Random Forest Regressor Ensemble method, handles non-linearity
Gradient Boosting Regressor High accuracy ensemble
Neural Network Regressor Deep learning for complex patterns

Quick Start

using AiDotNet;
using AiDotNet.Regression;

// Sample data: predicting house prices
var features = new double[][]
{
    new[] { 1500.0, 3.0, 2.0 },  // sqft, bedrooms, bathrooms
    new[] { 2000.0, 4.0, 2.5 },
    new[] { 1200.0, 2.0, 1.0 }
};
var prices = new double[] { 300000, 450000, 200000 };

// Build and train
var result = await new AiModelBuilder<double, double[], double>()
    .ConfigureModel(new RandomForestRegressor<double>(nEstimators: 100))
    .ConfigurePreprocessing()
    .BuildAsync(features, prices);

// Predict
var predictedPrice = result.Predict(new[] { 1800.0, 3.0, 2.0 });
Console.WriteLine($"Predicted price: ${predictedPrice:N0}");

Next Steps

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