LogisticRegression
E97070
LogisticRegression is a scikit-learn machine learning estimator that models the probability of class membership using a linear decision boundary with logistic (sigmoid) or related link functions.
Statements (60)
| Predicate | Object |
|---|---|
| instanceOf |
Python class
→
classification algorithm → linear model → scikit-learn estimator → |
| assumesRelationship |
log-odds linear in features
→
|
| defaultMultiClass |
auto
→
|
| defaultPenalty |
l2
→
|
| defaultSolver |
lbfgs
→
|
| hasAttribute |
classes_
→
coef_ → intercept_ → |
| hasMethod |
decision_function
→
fit → predict → predict_proba → score → |
| linkFunctionFamily |
logit link
→
|
| models |
probability of class membership
→
|
| module |
sklearn.linear_model
→
|
| optimizationObjective |
logistic loss minimization with regularization
→
|
| parameter |
C
→
class_weight → dual → fit_intercept → intercept_scaling → l1_ratio → max_iter → multi_class → n_jobs → penalty → random_state → solver → tol → verbose → warm_start → |
| providedBy |
scikit-learn
→
|
| regularizationControlledBy |
C
→
|
| requiresFeatureScaling |
often beneficial
→
|
| supportsPenalty |
elasticnet
→
l1 → l2 → none → |
| supportsProbabilityEstimates |
True
→
|
| supportsSolver |
lbfgs
→
liblinear → newton-cg → sag → saga → |
| supportsTask |
L1-regularized logistic regression
→
L2-regularized logistic regression → binary classification → elastic-net regularized logistic regression → multiclass classification → multinomial logistic regression → one-vs-one classification (via wrappers) → one-vs-rest classification → probability estimation → |
| usesDecisionBoundaryType |
linear decision boundary
→
|
| usesLinkFunction |
logistic
→
sigmoid → |
Referenced by (1)
| Subject (surface form when different) | Predicate |
|---|---|
|
scikit-learn
→
|
hasConcept |