Logistic Regression

From Clariopedia

Contents 1 Overview 2 Usage 2.1 Input Stream 2.2 Configuration 2.2.1 Configuration Face 2.2.2 Attribute Selection 2.3 Field Definitions 2.4 Results 2.5 Output Stream 3 Video Demonstration 4 References

Overview

The clario^® node Logistic Regression is used to build a model for a binary (2 class) dependent attribute. The model outcome is a prediction of the likelihood to belong to one of the two classes.

Note: The class (dependent) attribute must be defined as a string attribute in the read file node.

Usage

Input Stream

The node connector can be connected to a variety of nodes, (ie. Read, Aggregate, Append, Missing, etc.), but requires a valid stream of data.

Configuration

The Logistic Regression node has two configuration faces, configuration and attribute selection

Configuration Face

The configuration face contains five different sections requiring your interaction. Choosing a Selection Method along with a Class Attribute from the drop down box, typing in a Class Success Value along with Class Failure Value, and lastly selecting a Weight Attribute, if any, from the drop down box. These five sections must be completed in order to run the logistic node.

Attribute Selection

The Attribute Selection face is dependent on what has been selected for Selection Method in the previous configuration face. Attribute Selection involves selecting a desired attribute(s) by clicking on them in the Available Attributes box and dragging Attributes into Force Entry Attributes. The selected attributes will be highlighted once clicked. If 'Stepwise' is selected from the Selection Method drop down box, the Candidate Attributes box will also be present along with Available Attributes and Force Entry Attributes.

NOTES: To efficiently find attribute names, begin typing an attribute name in the text box directly above Available Attributes. You will then be directed to the attribute(s) beginning with the letter(s) you type. To select multiple attributes at once, either use [Ctrl]+click to select multiple, one at a time, [Shift]+down arrow to select multiple in order of appearance, or use [Shift]+click to select the beginning and the ending attribute which will select all attributes. To de-select an attribute click on the attribute in the Selected Attributes box and drag and drop into the Available Attributes box. Attributes in the Selected Attributes list can be re-ordered by clicking and holding on an attribute and dragging it to the desired position within the Selected Attributes box.

Field Definitions

• Valid Inputs – You must link to a valid data stream (ie. Read, Append, Filter, etc.).
• Attributes – You must select at least one attribute.
• You must provide a Class Success value.
• You must provide a Class Failure value.

• "Class Attribute" cannot be null.
• "Class Attribute" must be string.
• "Weight Attribute" default value is <NULL>.
• "Weight Attribute" must be numeric.
• If an attribute is in "Weight Attribute" it cannot be available on the second node face (Attribute Selection).

Results

There is one results face with two tabs (Detailed Results and Analysis of Maximum Likelihood Estimates) for the logistic regression node. Detailed Results – This pop up box contains statistics such as frequency and weight of the class attribute, Model Fit Statistics, and Global Fit Statistics such as Chi-square. Analysis of Maximum Likelihood Estimates – Contains one row of data for each attribute included in the model along with its respective degrees of freedom, model estimate, model coefficient, and significance level.

Output Stream

The results from logistic can be read into Write, Score, and Evaluate. The results tables can also be exported into Excel.

Video Demonstration

References

None.