Regression

Regression models predict a numeric output for each input row. At inference time, provide tabular data with the same feature columns used during training - the model returns a continuous predicted value.

Available Models

• Linear Regression – Baseline linear model, fast and interpretable
• Ridge Regression – Linear regression with L2 regularization
• Lasso Regression – Linear regression with L1 regularization and feature selection
• ElasticNet Regression – Combined L1 + L2 regularization
• Polynomial Regression – Linear regression on polynomial feature expansions
• Decision Tree – Single tree-based regressor
• Random Forest – Ensemble of decision trees for robust predictions
• Support Vector Regression – SVR with configurable kernel functions
• K-Nearest Neighbors – Predict by averaging similar training examples
• Extra Trees – Randomized ensemble regressor
• AdaBoost – Boosting ensemble for regression
• Gradient Boosting – Sequential boosting with high accuracy
• XGBoost – Optimized gradient boosting
• LightGBM – Fast gradient boosting with leaf-wise growth
• CatBoost – Gradient boosting with categorical feature support
• Multi-Layer Perceptron – Neural network for complex regression
• Huber Regression – Robust regression resistant to outliers