A logistic regression is a supervised machine learning method for solving classification problems. The probability of the objective being a particular class is modeled as the value of a logistic function, whose argument is a linear combination of feature values. The complete and updated reference with all available parameters is in our documentation website.
Inheritance: Response
LogisticRegression Class Documentation
Beispiel #1
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 /// <summary>
 /// Creates a Logistic Regression.
 /// </summary>
 /// <param name="dataset">A DataSet instance</param>
 /// <param name="name">The name you want to give to the new logistic regression. </param>
 /// <param name="arguments">Specifies the id of the field that you want to predict.</param>
 public Task<LogisticRegression> CreateLogisticRegression(DataSet dataset, string name = null,
                                     LogisticRegression.Arguments arguments = null)
 {
     arguments = arguments ?? new LogisticRegression.Arguments();
     if (!string.IsNullOrWhiteSpace(name))
         arguments.Name = name;
     arguments.DataSet = dataset.Resource;
     return Create<LogisticRegression>(arguments);
 }
Beispiel #2
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 /// <summary>
 /// Creates a LogisticRegression using supplied arguments.
 /// </summary>
 public Task<LogisticRegression> CreateLogisticRegression(LogisticRegression.Arguments arguments)
 {
     return Create<LogisticRegression>(arguments);
 }