Train Random Forest and XGBoost regression models on tabular data.
npx skills add https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000 --skill ml-regressor
Please help me install this Agent Skill. Skill: ml-regressor Source: https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000/tree/main/.github/skills/ml-regressor Command: npx skills add https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000 --skill ml-regressor
This Skill automates nonlinear regression modeling and interpretation by training Random Forest and XGBoost models on tabular data, delivering predictions and insights.
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.
High-intent search queries and answers about installing and using this skill.
To train Random Forest and XGBoost regression models, load your tabular features and target, then call fit with tune_hyperparams=True to automatically handle nonlinear relationships and hyperparameter tuning for accurate predictions.
Feature importance analysis in ensemble regression identifies which input variables most strongly drive model predictions. This Skill computes and visualizes these importances after training Random Forest or XGBoost models to help interpret complex feature interactions.
Yes, cross-validation and hyperparameter tuning are supported for nonlinear regression. The Skill applies these techniques during model training to improve accuracy and ensure robust generalization across structured datasets.
This approach works for both exploratory data analysis and production-ready predictions. It supports workflows across finance, engineering, and research by training ensemble models on structured datasets with nonlinear relationships.
The best way to visualize feature importances from XGBoost models is using the Skill's built-in visualization outputs. After calling fit and evaluating on test data, it generates publication-ready visualizations of the key features driving model predictions.
bolun-ben-shipGenerate SEO-optimized competitor comparison and alternatives pages with schema markup.
JunMA98Generate publication-ready CS visualizations with accessible color palettes and export formats.
Vishnugupta2711Train XGBoost and LightGBM models on tabular datasets with early stopping and cross-validation.
Ritabrata-ChakrabortyFit statistical models and analyze time series data in Python.
