ml-regressor

What problem does it solve?

This Skill automates nonlinear regression modeling and interpretation by training Random Forest and XGBoost models on tabular data, delivering predictions and insights.

Core Features & Use Cases

• Ensemble regression with Random Forest and XGBoost, capable of handling nonlinear relationships.
• Feature importance analysis, cross-validation, and basic hyperparameter tuning to improve accuracy.
• Use Case: From exploratory data analysis to production-ready models across finance, engineering, and research.

Quick Start

Load your dataset as X (features) and y (target), instantiate the model with model_type='rf' or 'xgb', call fit(X_train, y_train, tune_hyperparams=True), then evaluate on test data and visualize feature importances.