Linear Regression
Baseline linear model for continuous value prediction
Linear Regression fits a weighted sum of input features to predict a continuous target. It is the fastest and most interpretable regression model, serving as a reliable baseline for any regression task.
When to use:
- Establishing a baseline before trying complex models
- When feature-to-target relationships are approximately linear
- When coefficient interpretability is required
Input: Tabular data with the feature columns defined during training Output: Continuous predicted value
Model Settings (set during training, used at inference)
Fit Intercept (default: true) Whether to add an intercept (bias) term. Disable only if data is pre-centered.
Normalize (default: false) Whether to normalize features before fitting (deprecated in newer sklearn; use a scaler in the pipeline instead).
Inference Settings
No dedicated inference-time settings. The trained weight vector is applied to input features.