Logistic regression machine learning (two): API calls --sklearn.linear_model.LogisticRegression logistic regression parameters are in what is the meaning

Logistic regression requires knowledge points

• We know the logistic regression function loss
• Optimization of logistic regression to know
• We know sigmoid function
• You know the scenario logistic regression
• Application LogisticRegression realize logistic regression forecast
• We know the difference between precision, recall indicators
• We know how to solve assess imbalances in the sample
• ROC curve to understand the meaning of instructions AUC index size
• Classification_report precise application, recall calculated
• Indicators for computing applications roc_auc_score

Logistic regression introduced api

• sklearn.linear_model.LogisticRegression(solver=‘liblinear’, penalty=‘l2’, C = 1.0)
  • Optional solver parameters: { 'liblinear', 'sag', 'saga', 'newton-cg', 'lbfgs'},
    • Default: 'liblinear'; algorithm for optimization problems.
    • For small data sets, "liblinear" is a good choice, "sag" and 'saga' For large datasets faster.
    • For many types of problems, only 'newton-cg', 'sag', 'saga' and 'lbfgs' can handle a number of loss; "liblinear" only "one-versus-rest" classification.
  • penalty: regularization of the kind of regularization term also called a penalty term, because too fine a model to prevent, to punish it
  • C: regularization efforts and linear regression alpha is a reason

The small number of default categories as positive examples

LogisticRegression method is equivalent SGDClassifier (loss = "log", penalty = ""), SGDClassifier implements a common stochastic gradient descent learning. The use LogisticRegression achieved SAG,