fitintercept

fit_intercept is a boolean parameter used by many linear models to control whether the model should learn an intercept (also called a bias term) as part of the fitting process. When the flag is enabled, the model estimates a constant term that shifts the decision boundary or linear prediction.

If fit_intercept is True, the predictive equation takes the form y = X w + b, where w is the vector of feature coefficients and b is the intercept. If fit_intercept is False, the model assumes the intercept is zero and fits y ≈ X w without an additive bias term. In that case, the interpretation of the coefficients changes: predictions are anchored to the origin in feature space.

In practice, many linear models in libraries such as scikit-learn (for example LinearRegression, Ridge, Lasso, ElasticNet, and LogisticRegression) expose fit_intercept as a parameter. After fitting, the learned intercept is typically stored in an attribute named intercept_. When fit_intercept is False, intercept_ is usually zero.

The setting also interacts with regularization and data preprocessing. Regularization typically applies to the coefficients, while the intercept is often not regularized. When fit_intercept is True, the model can accommodate a nonzero baseline, which can be important when the target variable has a nonzero mean or when features are not centered. Conversely, setting fit_intercept to False is appropriate when the data are centered or when domain knowledge indicates the relationship should pass through the origin.

Practical guidance: choose True if you expect a meaningful baseline offset; choose False if you know the relationship should have no intercept or if you have preprocessed data that makes an intercept unnecessary. Proper scaling and, if needed, centering, should be considered to ensure meaningful and stable estimates.