Logistic Regression VAL

In Logistic Regression, a set of independent variables (in this case, columns) is processed to predict the value of a dependent variable (column) that assumes two values referred to as response (1) and non-response (0).

Options

Backward Only: Specifies whether to use only backward technique or not, i.e. a forward step is not performed.
Backward: Specifies whether to take backward steps or not. Backward steps, i.e., removing variables from a model, use the P-value of the T-statistic, i.e., the ratio of a B-coefficient to its standard error.
Input columns: Specifies the name(s) of the column(s) representing the independent variables used in building a logistic regression model.
Conditional Independence Threshold: Specifies the condition index threshold value to use while generating near dependency report.
Constant: Specifies whether the logistic model includes a constant term or not.
Convergence: Specifies the convergence criterion such that the algorithm stops iterating when the change in log likelihood function falls below this value.
End Threshold: Specifies the ending threshold value utilized in the Multi-Threshold Success output.
Entrance Criterion: Specifies the criterion to enter a variable into the model. The W-statistic chi-square P-value must be less than this value for a variable to be added.
Forward Only: Specifies whether to use only forward technique or not, i.e. a backward step is not performed.
Forward: Specifies whether to use forward technique or not. Starting with no independent variables in the model, a forward step is performed, adding the 'best' choice, followed by a backward step, removing the worst choice
Group By columns: Specifies the name(s) of the column(s) dividing the input into partitions, one for each combination of values in the group by columns.
Increment Threshold: Specifies the difference in threshold values between adjacent rows in the Multi-Threshold Success output.
Lift Output: Specifies whether to build a lift chart or not and add it in the functions output string.
Matrix Data: Specifies the input matrix data to use for the analysis. Instead of internally building a matrix with the Matrix function each time this analysis is performed, the user may build an ESSCP Matrix.
Max Iteration: Specifies the maximum number of attempts to converge on a solution.
Memory Size: Specifies the memory size in megabytes to allocate for in-memory Logistic Regression.
XML Report: Specifies whether to produce an XML report showing columns that may be collinear as part of the output or not.
Remove Criterion: Specifies the criterion to remove a variable from the model. The T-Statistic P-value must be greater than this value for a variable to be removed.
Response Column: Specifies the name of the column that represents the dependent variable being predicted.
Response Value: Specifies the value assumed by the dependent column that is to be treated as the response value.
Sample: Specifies whether to use sample of the data to be read into memory for processing, if the memory size available is less than the amount of data to process.
Start Threshold: Specifies the beginning threshold value utilized in the Multi-Threshold Success output.
Stats Report: Specifies whether an optional data quality report should be delivered in the function's XML output string or not, which includes the mean and standard deviation of each model variable, derived from an ESSCP matrix.
Stepwise: Specifies whether to perform a stepwise procedure or not.
Sucess Output: Specifies whether an optional success report should be delivered in the function's XML output string or not, which includes the displaying counts of predicted versus actual values of the dependent variable of the logistic regression model.
Threshold Output: Specifies whether the Multi-Threshold Success output should be produced or not and included in the XML output string in the result.
Variance Proportion Threshold: Specifies the variance proportion threshold value to use while generating near dependency report.
Output Schema: Output Schema, if Volatile is true then use user login as the schema.
Output Table: Output Table
VAL Location: VAL Location

Logistic Regression VAL

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