Fit a Linear Regression and return results with coefficients and residuals.

Fit a Random Forest Regressor and return results with feature importances.

Fit a Support Vector Regressor and return results.

Fit an XGBoost Regressor and return results with feature importances.

Fit a Bayesian Ridge Regression.

Returns standard regression outputs plus Bayesian-specific posterior standard deviations and estimated precision parameters (alpha, lambda).

Fit a Gaussian Hidden Markov Model.

Unlike the regression functions, HMMs are unsupervised — there is no
target column.  The function discovers *n_states* hidden regimes in the
data and returns the decoded state sequence, transition matrix, and
per-state Gaussian parameters.