Fit a logistic regression model to user inputted data and outputs the predicted value for each of the observations in the data.
> **Note:** This is depreciated. Binary Classifier API is an example built with Microsoft Azure Machine Learning that fits a logistic regression model to data input by the user and then outputs the predicted value for each of the observations in the data. Suppose you have a dataset and would like to predict a binary dependent variable based on the independent variables. *Logistic Regression* is a popular statistical technique used for such predictions. Here the dependent variable is binary or dichotomous and 'p' is the probability of presence of the characteristic of interest. ##Sample Scenario## A simple scenario could be where the researcher is trying to predict whether the prospective student is likely to accept the admission offer to a university based on information (GPA in high school, family income, resident state, gender). The predicted outcome is the probability of a prospective student accepting their admission offer. *While this web service could be consumed by users - potentially through a mobile app, website, or even on a local computer for example, the purpose of the web service is also to serve as an example of how Azure ML can be used to create web services on top of R code. With just a few lines of R code and clicks of a button within the Azure ML Studio, an experiment can be created with R code and published as a web service. The web service can then be published to the Azure Marketplace and consumed by users and devices across the world with no infrastructure set-up by the author of the web service.*