{"id":35507,"date":"2019-03-01T06:00:04","date_gmt":"2019-03-01T13:00:04","guid":{"rendered":"http:\/\/devblogs.microsoft.com\/premier-developer\/?p=35507"},"modified":"2019-02-28T20:59:59","modified_gmt":"2019-03-01T03:59:59","slug":"exploring-feature-weights-using-r-and-azure-machine-learning-studio","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/premier-developer\/exploring-feature-weights-using-r-and-azure-machine-learning-studio\/","title":{"rendered":"Exploring Feature Weights using R and Azure Machine Learning Studio"},"content":{"rendered":"

In this post, Senior App Dev Manager <\/span>Randy Park<\/span><\/a> performs an exploratory data analysis using R and Azure Machine Learning Studio.<\/span><\/span><\/p>\n

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Suppose we have to design a black box which will display a \u201cthumbs up\u201d<\/span> or \u201cthumbs down\u201d depending on hundreds of different combinations of inputs, we probably have a general idea of utilizing machine learning methodologies designing of the black box, here we call it a model. But often, more important question than how to build the model is rather how we can find out by looking at the proposed model, which of the input is more important than others. What is the weightiest input or feature to drive the outcome? We may think such problem is rather simple in this day and age, where abundance of machine learning algorithms and models<\/a> are readily available. Once we start to analyze the each of implementation deeper and engage into coding or hands-on exercise, we come to appreciate the simple abstractions on the complexities underneath. We, however, will see outcome of design approach may not necessarily result the same outcome, and digging in details may provide further insights on the exploratory data analysis (EDA)<\/a>.<\/p>\n

Objective<\/strong>: Put it more practically, the exercises in this article will utilize a very popular exercise\u00a0dataset to conduct EDA<\/a> to determine factors that lead to attrition. The snapshot of data with first 12 columns, out of 35 total columns is as below.:<\/p>\n

<\/p>\n

We will compare high level findings by common implementations in R vs. utilizing Machine Learning modules provided by Azure Machine Learning Studio.<\/p>\n

R implementation using Microsoft R Client or R Studio<\/strong><\/span><\/a><\/p>\n

Using R, we will utilize simple finding of correlation coefficients to locate highest correlating coefficients with the Attrition.<\/p>\n

Azure Machine Learning Studio<\/span><\/a><\/strong><\/p>\n

We will utilize Azure machine learning studio to utilize simple and powerful permutation feature importance<\/a>.<\/p>\n

There are many implementations of above-mentioned dataset; anyone can find abundance of implementations linked to the dataset hosted in kaggle.com<\/a>. Practical outcome of such exploratory data analysis is to identify at least the top three or four factors that contribute to employee turnover. Each of the analysis should be backed up by robust experimentation and where applicable, the appropriate visualization and you will find plenty examples in kaggle.com and others alike.<\/p>\n

1. R implementations using with Microsoft R Client or R Studio<\/strong><\/span><\/p>\n

Follow installation of Microsoft R client instructions in this Microsoft R Client introduction documentation<\/a>. Or you can choose to utilize popular OSS tool of R Studio<\/a>.<\/p>\n

Below steps are not<\/span> necessarily the entire code and not naming all the R package requirements, rather highlights the key details of implementation in R.<\/p>\n

1. Import Data and prepare the data frame<\/a> to operate computations.<\/p>\n

## Import the data\r\ncase_data<-data.frame(read_excel(\"Data\/attrition-data.xlsx\"))<\/pre>\n
2. Preprocess<\/p>\n
We need to remove non value-added variables from the dataset<\/p>\n