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Features

Supervised analysis

available

Train and evaluate models using a known target for classification or regression.

Overview

Supervised analysis uses an existing target variable to learn relationships between selected features and an observed outcome.

Ounias preserves the target, feature roles, split configuration, model parameters, predictions, evaluation results, and derived analytical artifacts.

Supported problem types

Classification

Predict a discrete category or label.

  • Binary classification
  • Multiclass classification
  • Class-level evaluation

Regression

Predict a continuous numerical outcome.

  • Continuous predictions
  • Regression-specific evaluation
  • Downstream discretization where another analysis requires finite states

Supported methods

Linear and probabilistic

Logistic Regression

available

A linear supervised model commonly used as an interpretable classification baseline.

Classification Linear

Naive Bayes

available

A probabilistic classifier based on conditional feature distributions.

Classification Probabilistic

Tree based

Random Forest

available

An ensemble of decision trees capable of capturing nonlinear relationships and interactions.

Classification Regression Ensemble

LightGBM

available

Gradient-boosted decision trees for higher-capacity supervised modeling.

Classification Regression Boosting

CatBoost

available

Gradient-boosted decision trees with support for complex nonlinear supervised relationships.

Classification Regression Boosting

Instance based

K-Nearest Neighbors

available

Predicts outcomes using nearby observations in the selected feature space.

Classification Regression Distance based

Workflow

  1. 01

    Select the target

    Choose the observed outcome the model should learn to predict.

  2. 02

    Define the features

    Select the variables available to the model and configure their analytical roles.

  3. 03

    Train and evaluate

    Fit the model and evaluate predictions using the configured split.

  4. 04

    Explain and compare

    Inspect metrics, errors, explanations, and differences across models.

Evaluation and enrichment

  • Held-out performance evaluation
  • Standardized metric pillars
  • Cross-method comparison
  • Feature explanations
  • Rule extraction
  • Prediction and error inspection
  • Stability analysis
  • Longitudinal analysis where applicable

Interpretation

  • Predictive performance does not establish causal relationships.
  • Evaluation quality depends on the target, split design, dataset, and feature configuration.
  • Model metrics should be interpreted together with domain relevance and potential data limitations.