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Logistic Regression Assumptionlogittransformation4 Recall that one assumption of logistic regression is that the logit transformation of the probabilities of the target variable results in a linear relationship with the input variables.

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Missing ValuesInputs???Cases???5??? Regression uses only full cases in the model. This means that any case, or observation, that has a missing value will be excluded from consideration when building the model. As discussed earlier, when there are many potential input variables to be considered, this could result in an unacceptably high loss of data. Therefore, when possible, missing values should be imputed prior to running a regression model.

Other reasons for imputing missing values include the following: ? Decision trees handle missing values directly, whereas regression and neural network models ignore all observations with missing values on any of the input variables. It is more appropriate to compare models built on the same set of observations. Therefore, before doing a regression or building a neural network model, you should perform data replacement, particularly if you plan to compare the results to results obtained from a decision tree model.

? If the missing values are in some way related to each other or to the target variable, the models created without those observations may be biased.