Random Forest Regression Task with automatic hyperparameter tuning.

This task performs Random Forest regression with cross-validation to find optimal hyperparameters (n_estimators and max_depth). It provides comprehensive outputs including metrics, feature importances, and visualization plots.

Inputs: - data: Input table containing features and target variable

Outputs: - summary_table: Performance metrics (R², RMSE) for train and test sets - vip_table: Feature importance scores ranked by importance with correlation signs - plot_estimators: Cross-validation performance vs hyperparameters - vip_plot: Bar plot of feature importances colored by correlation sign - plot_train_set: Predicted vs true values for training set - plot_test_set: Predicted vs true values for test set

Configuration: - target: Name of the target column to predict - columns_to_exclude: List of column names to exclude from analysis (optional) - test_size: Proportion of data to use for testing (0.0 to 1.0)