Logistic Regression With R

Raghavan Madabusi runs through a sample logistic regression:

Input Variables: These variables are called as predictors or independent variables.

  • Customer Demographics (Gender and Senior citizenship)
  • Billing Information (Monthly and Annual charges, Payment method)
  • Product Services (Multiple line, Online security, Streaming TV, Streaming Movies, and so on)
  • Customer relationship variables (Tenure and Contract period)

Output Variables: These variables are called as response or dependent variables. Since the output variable (Churn value) takes the binary form as “0” or “1”, it will be categorized under classification problem in the supervised machine learning.

One of the interesting things in this post was the use of missmap, which is part of Amelia.

Related Posts

Logistic Regression In R

Steph Locke has a presentation on performing logistic regression using R: Logistic regressions are a great tool for predicting outcomes that are categorical. They use a transformation function based on probability to perform a linear regression. This makes them easy to interpret and implement in other systems. Logistic regressions can be used to perform a classification […]

Read More

Feature Improvements In Microsoft R Server 9.1

David Smith gives us a nice roundup of feature improvements in Microsoft R Server 9.1: Interoperability between Microsoft R Server and sparklyr. You can now use RStudio’s sparklyr package in tandem with Microsoft R Server in a single Spark session New machine learning models in Hadoop and Spark. The new machine learning functions introduced with Version 9.0 […]

Read More

Leave a Reply

Your email address will not be published. Required fields are marked *

Categories

April 2017
MTWTFSS
« Mar  
 12
3456789
10111213141516
17181920212223
24252627282930