Data Mining - Professional SQL Server Analysis Services 2005 with MDX - page 731

cision Tree will be set to a value greater than the number of input cases
used to train the model. The linear regression algorithm will typically
be used when you want to find the relationship between two continuous
columns. The algorithm finds the equation of a line that best fits data
representing the relationship between the input columns. Microsoft Lin-
ear Regression algorithm only supports input columns that have certain
content type. The main content type typically used will be continuous.
The algorithm does not support content types discrete or discretized.
For more details on the content type supported by the algorithm please
refer to product documentation.
Microsoft Logistic Regression
The Microsoft Logistic Regression is a variation of the Microsoft Neural
Networks algorithm where the hidden layer is not present. The simplest
form of logistic regression is to predict a column that has two states.
The input columns can contain many states and can be of many content
types (discrete, continuous, discretrized, etc.). You can certainly model
such a predictable column using linear regression but the linear regres-
sion might not restrict the values to the minimum and maximum values
of the column. However, logistic regression is able to restrict the output
values for the predictable column to the minimum and maximum values
with the help of a S-shaped curve instead of the linear line which would
have been created by a linear regression. In addition, logistic regression
is able to predict columns of content type discrete or discretized and
able to take input columns that are content type discrete or discretized.