Regularized Logistic Regression Algorithm
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A Regularized Logistic Regression Algorithm is a Logistic Regression Algorithm that is a Regularized Optimization Algorithm.
- Context:
- It can range from being an L2-Regularized Logistic Regression Algorithm to being a L1-Regularized Logistic Regression Algorithm.
- It can be applied by a Regularized Logistic Regression System, such as sklearn.LogisticRegression.
- See: Regularized Least Squares Algorithm.
References
2014
- http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
- QUOTE: ... The LogisticRegression class can be used to do L1 or L2 penalized logistic regression. L1 penalization yields sparse predicting weights. For L1 penalization sklearn.svm.l1_min_c allows to calculate the lower bound for C in order to get a non “null” (all feature weights to zero) model.