Regression Nodes

Regression nodes predict quantitative targets from spectra or features.

Training and Prediction Nodes

Node	Use When	Inputs	Outputs	Key Configuration
Train PLS Regression (model.pls)	Standard multivariate calibration for concentrations or quantitative properties.	X: Array2D; y: TargetMatrix?	model; X_scores; Y_scores; X_loadings; Y_loadings; y_pred; y_true; y_pred_cv; vip; coefficients	n_components; scale; cv_method; cv_folds. Requires SpectroChemPy. Choose components by CV error and residual diagnostics, not calibration R² alone.
Apply PLS Regression Model (model.pls_predict)	Apply a fitted PLS model to new spectra.	X_new: SpectralDataset; model: RegressionModel	y_pred: TargetMatrix	no parameters. New spectra must receive the same preprocessing as training spectra.
Train PCR Regression (model.pcr)	Regress targets on PCA scores when you want explicit PCA compression before regression.	X: Array2D; y: TargetMatrix?	model; scores; loadings	n_components; scale.
Train SVR Regression (model.svr)	Nonlinear calibration with support vector regression.	X: Array2D; y: TargetMatrix?	model; predictions; residuals	kernel; C; epsilon; gamma; degree; coef0; scale. Scaling is usually important for SVR.
Train Linear Regression (model.linear_regression)	Simple baseline calibration or already-selected low-dimensional features.	X: Array2D; y: TargetMatrix?	model; predictions; residuals	fit_intercept.
Apply Saved Model Artifact (model.load_apply)	Load a saved model artifact and apply it to inference data.	X_new: SpectralDataset; model_ref: ModelReference?	result; labels; model_id	model_id.

Key Outputs

PLS regression exposes interpretation-oriented outputs such as scores, loadings, training predictions, cross-validated predictions, VIP scores, and regression coefficients. For calibration review, prefer y_pred_cv and holdout predictions over the optimistic fitted y_pred.

scikit-learn documents the core PLSRegression parameters, including n_components and scale, here: https://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html.

Decision Notes

• Use PLS when spectra are collinear and the target is quantitative.
• Use PCR when you want PCA compression to be explicit and separately inspected.
• Use SVR only after careful scaling and validation; nonlinear models can overfit small spectral datasets.
• Use linear regression mainly for selected variables, peak areas, or low-dimensional features.
• Keep sample-target alignment explicit. If targets arrive separately, use Data/Attach Target or a workflow that preserves row identity.

Good Practice

Confirm sample-target alignment before training. For spectroscopy calibration, this is as important as model choice.