Classification

Classification models predict which category a data point belongs to. At inference time, provide tabular data with the same feature columns used during training - the model returns a class label and confidence scores.

Available Models

• Logistic Regression – Linear model for binary and multi-class classification
• Ordinal Logistic Regression – Logistic regression for ordered/ranked categories
• Random Forest – Ensemble of decision trees for robust classification
• Decision Tree – Single interpretable tree-based classifier
• K-Nearest Neighbors – Classify based on similarity to training examples
• Support Vector Machine – Hyperplane-based classifier for complex boundaries
• Naive Bayes – Probabilistic classifier based on Bayes' theorem
• Extra Trees – Randomized ensemble with fast training
• AdaBoost – Boosting ensemble that focuses on hard examples
• Gradient Boosting – Sequential boosting with strong accuracy
• XGBoost – Optimized gradient boosting for tabular data
• LightGBM – Fast gradient boosting with leaf-wise growth
• CatBoost – Gradient boosting with native categorical feature handling
• Multi-Layer Perceptron – Neural network classifier for complex patterns