Creating Regression model

From a workbook, click Actions > ML Dashboard. The ML Dashboard page is displayed. From the Create Model section, click Regression and specify the following:

Feature	Linear Regression
Model Type	Generalized linear model
Best Suited For	Linearly related continuous targets; when interpretability is key
Relationship Modeling	Assumes a linear relationship between predictors and target
Handling of Interactions	Requires manual inclusion of interaction terms
Interpretability	Very high – clear coefficient estimates and significance tests
Parameter Tuning	Minimal – primarily choice of regularization (e.g., L1/L2 penalties)
Data Requirements	Needs homoscedasticity, no multicollinearity, and normally distributed errors
Handling of Outliers	Sensitive to outliers; robust variants (e.g., RANSAC) can mitigate
Multicollinearity	Problematic – variance inflation; requires feature selection or PCA
Feature Importance	Directly from coefficient magnitudes
Library Support	scikit-learn (LinearRegression, Ridge, Lasso), statsmodels
Prediction Output	Continuous point estimates (with optional confidence intervals)
Deployment Readiness	Extremely lightweight; instantaneous inference; minimal memory footprint
External Features	New features simply added as new coefficients
Community & Resources	Extensive academic literature; ubiquitous baseline for regression tasks

Click Create and commence the model training process.