Scikit-Learn Regressors Reference

Quick lookup for commonly utilized continuous machine learning algorithms within sklearn.

Linear Models

LinearRegression

Use Case: Baseline continuous relationships. Key Parameters: None (Standard Ordinary Least Squares).

Ridge and Lasso

Use Case: Regularised linear regression to drastically prevent overfitting. Key Parameters: * alpha: Regularisation strength (Larger means stronger penalty).

Tree-Based Models

DecisionTreeRegressor

Use Case: Non-linear rule-based regression. Key Parameters: * max_depth: Limits geometric tree depth.

RandomForestRegressor

Use Case: Averaged, robust ensemble predictions. Key Parameters: * n_estimators: Count of trees constructed. * max_features: Maximum variables considered at each dynamic split.

GradientBoostingRegressor

Use Case: Corrective step-wise ensemble. Key Parameters: * learning_rate: Shrinkage parameter. * loss: Loss function to minimize (e.g., 'squared_error').

Advanced Models

SVR (Support Vector Regressor)

Use Case: Geometric tube boundary analysis. Key Parameters: * kernel: Space transformation ('linear', 'rbf'). * C: Margin strictness. * epsilon: The mathematical width of the no-penalty tube.

KSB Mapping

KSB	Description	How This Addresses It
K4.1	Statistical models and methods	Understanding the statistical basis of regression and classification
K4.2	ML and AI techniques	Implementing and comparing supervised learning algorithms
K4.4	Resource constraints and trade-offs	Model complexity vs interpretability; computational cost
S1	Scientific methods and hypothesis testing	Formulating hypotheses and testing with rigorous validation
S4	Building models and validating	Cross-validation, train/test evaluation, performance metrics
B5	Impartial, hypothesis-driven approach	Honest evaluation of model performance and limitations