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Apache Mahout Essentials by Jayani Withanawasam

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Logistic regression with SGD

Logistic regression solves problems that require the target variable to be a discrete value / categorical target variable. For example, a person's gender (male or female) can be a discrete output variable.

Even though a continuous outcome variable generated in linear regression can be converted to a categorical variable, it is not advisable to do so as it can drastically reduce the precision of the results.

Logistic regression can be applied in any of the following situations:

  • When there is a nonlinear relationship between the predictor variables and the target variable
  • When the variance of errors is not constant

The fundamental principle behind the logistic regression algorithm (using the maximum likelihood estimation) ...

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