Logistic Regression Tool

Connect an Alteryx data stream or XDF metadata stream that includes a target field of interest along with one or more possible predictor fields.

If the input data is from an Alteryx data stream, then the open source R glm function and the glmnet and cv.glmnet functions (from the glmnet package) is used for model estimation.

If the input data comes from either an XDF Output Tool or XDF Input Tool, then the RevoScaleR rxLogit function is used for model estimation. The advantage of using the RevoScaleR based function is that it allows much larger (out of memory) datasets to be analyzed, but at the cost of additional overhead to create an XDF file and the inability to create some of the model diagnostic output that is available with the open source R functions, and it only allows for the use of a logit link function.

• Type model name: Type a name for the model to identify the model when it is referenced in other tools. Model names must start with a letter and may contain letters, numbers, and the special characters period (.) and underscore (_). No other special characters are allowed, and R is case sensitive.
• Select target variable: Select the data to be predicted. A target variable is also known as a response or dependent variable.
• Select predictor variables: Select the data to use to influence the value of the target variable. A predictor variable is also known as a feature or an independent variable. Any number of predictor variables can be selected, but the target variable should not also be a predictor variable.

Columns containing unique identifiers, such as surrogate primary keys and natural primary keys, should not be used in statistical analyses. They have no predictive value and can cause runtime exceptions.

Click Customize to modify the Model, Cross-validation, and Plots settings.

• Use sampling weights in model estimation: Select a variable to determine the amount of importance to place on each record when creating a model estimation. If a value is used as both a predictor and a weight variable, the weight variable displays in the model call in the report output with the string "Right_" prepended to it.
• Use regularized regression: Select to balance the same minimization of sum of squared errors with a penalty term on the size of the coefficients and produce a simpler model.
• Enter value of alpha: Select a value between 0 (ridge regression) and 1 (lasso) to measure the amount of emphasis given to the coefficient.
• Standardize predictor variables: Select to make all variables the same size based on the algorithm used.
• Use cross-validation to determine model parameters: Select to perform cross-validation and obtain various model parameters
• Number of folds: Select the number of folds to divide the data. A higher number of folds results in more robust estimates of model quality, but fewer folds make the tool run faster.
• What type of model: Select the type of model to determine the coefficients.
• Simpler model
• Model with lower in sample standard error
• Set seed: Select to ensure the reproducibility of cross-validation and select the value of the seed used to assign records to folds. Choosing the same seed each time the workflow is run guarantees that the same records will be in the same fold each time.The value must be a positive integer.
• Select model type: Select the type of model to use for predicting the target variable.
• logit
• probit
• complementary log-log

• Use cross-validation to determine estimates of model quality: Select to perform cross-validation and obtain various model quality metrics and graphs. Some metrics and graphs will be displayed in the static R output, and others will be displayed in the interactive I output.
• Number of folds: Select the number of folds to divide the data. A higher number of folds results in more robust estimates of model quality, but fewer folds make the tool run faster.
• Number of trials: Select the number of times to repeat the cross-validation procedure. The folds are selected differently in each trial, and the overall results are averaged across all the trials. A higher number of folds results in more robust estimates of model quality, but fewer folds make the tool run faster.
• Enter positive class for target variable: Some of the measures reported by the tool in binary classification cases (such as true positive rate) require a positive class to be designated. To perform binary classification, type one of the two positive classes of the target variable. If left blank, one of the classes is automatically determined as the positive class. This option is only available for classification models.
• Use stratified cross-validation: Select so each fold has the same percentage of each class as is present in the entire dataset. This option is only available for classification models.
• Set seed: Select to ensure the reproducibility of cross-validation and select the value of the seed used to assign records to folds. Choosing the same seed each time the workflow is run guarantees that the same records will be in the same fold each time.The value must be a positive integer.

• Graph resolution: Select the resolution of the graph in dots per inch: 1x (96 dpi); 2x (192 dpi); or 3x (288 dpi). Lower resolution creates a smaller file and is best for viewing on a monitor. Higher resolution creates a larger file with better print quality.

Connect a Browse tool to each output anchor to view results.

• O (Output): Displays the model name and size of the object in the Results window.
• R (Report): Displays a summary report of the model that includes a summary and plots.
• I (Interactive): Displays an interactive dashboard of supporting visuals that allows you to zoom, hover, and click.

Connect an in-database data stream that includes a target field of interest along with one or more possible predictor fields.

If the input is from a SQL Server or Teradata in-database data stream, then the Microsoft Machine Learning Server rxLogit function (from the RevoScaleR package) is used for model estimation. This allows the processing to be done on the database server, as long as both the local machine and the server have been configured with Microsoft Machine Learning Server, and can result in a significant improvement on performance.

If the input is from an Oracle in-database data stream, then the Oracle R Enterprise ore.lm function (from the OREmodels package) is used for model estimation. This allows the processing to be done on the database server, as long as both the local machine and the server have been configured with Oracle R Enterprise, and can result in a significant improvement on performance.

For an in-database workflow in an Oracle database, full functionality of the resulting model object downstream only occurs if the Logistic Regression tool is connected directly from a Connect In-DB tool with a single full table selected, or if a Write Data In-DB tool is used immediately before the Logistic Regression tool to save the estimation data table to the database. Oracle R Enterprise makes use of the estimation data table to provide full model object functionality, such as calculating prediction intervals.

• Model name: Each model needs to be given a name so it can later be identified. The choice is to either provide a name, or have a name automatically generated. Model names must start with a letter and may contain letters, numbers, and the special characters period (".") and underscore ("_"). No other special characters are allowed, and R is case sensitive.
• Select the target variable: Select the field from the data stream you want to predict.
• Select the predictor variables: Choose the fields from the data stream you believe "cause" changes in the value of the target variable.

Columns containing unique identifiers, such as surrogate primary keys and natural primary keys, should not be used in statistical analyses. They have no predictive value and can cause runtime exceptions.

• Omit a model constant: Check this item if you want to omit a constant from the model. This should be done if there is an explicit reason for doing so.
• Oracle specific options: This option allows for the configuration of additional options only relevant for the Oracle platform.
• Model type: Select the type of model to use for predicting the target variable.
• logit
• probit
• complementary log-log
• Save the model to the database: Causes the estimated model object to be saved in the database, and is recommended so that the model objects and estimation tables live together in a centralized location in the Oracle database.
• Use sampling weights for model estimation: Click the check box and then select a weight field from the data stream to estimate a model that uses sampling weight. A field is used as both a predictor and the weight variable, then the weight variable will appear in the model call in the output with the string "Right_" prepended to it.
• Teradata specific configuration: Microsoft Machine Learning Server needs additional configuration information about the specific Teradata platform to be used – in particular, the paths on the Teradata server to R's binary executables, and the location where temporary files that are used by Microsoft Machine Learning Server can be written. This information will need to be provided by a local Teradata administrator.

View the output

Connect a Browse tool to each output anchor to view results.

• O anchor: Output. Displays the model name and size of the object in the Results window.
• R anchor: Report. Displays a summary report of the model that includes a summary and plots.