Book Image

SQL Server 2017 Machine Learning Services with R.

By : Julie Koesmarno, Tomaž Kaštrun
Book Image

SQL Server 2017 Machine Learning Services with R.

By: Julie Koesmarno, Tomaž Kaštrun

Overview of this book

R Services was one of the most anticipated features in SQL Server 2016, improved significantly and rebranded as SQL Server 2017 Machine Learning Services. Prior to SQL Server 2016, many developers and data scientists were already using R to connect to SQL Server in siloed environments that left a lot to be desired, in order to do additional data analysis, superseding SSAS Data Mining or additional CLR programming functions. With R integrated within SQL Server 2017, these developers and data scientists can now benefit from its integrated, effective, efficient, and more streamlined analytics environment. This book gives you foundational knowledge and insights to help you understand SQL Server 2017 Machine Learning Services with R. First and foremost, the book provides practical examples on how to implement, use, and understand SQL Server and R integration in corporate environments, and also provides explanations and underlying motivations. It covers installing Machine Learning Services;maintaining, deploying, and managing code;and monitoring your services. Delving more deeply into predictive modeling and the RevoScaleR package, this book also provides insights into operationalizing code and exploring and visualizing data. To complete the journey, this book covers the new features in SQL Server 2017 and how they are compatible with R, amplifying their combined power.
Table of Contents (12 chapters)

Functions for statistical tests and sampling

Statistical tests are important for determining the correlation between two (or more) variables and what is their direction of correlation (positive, neutral, or negative). Statistically speaking, the correlation is a measure of the strength of the association between two variables and their direction. The RevoScaleR package supports calculation of Chi-square, Fischer, and Kendall rank correlation. Based on the types of variable, you can distinguish between Kendall, Spearman, or Pearson correlation coefficient.

For Chi-Square test, we will be using the rxChiSquareTest() function that uses the contingency table to see if two variables are related. A small chi-square test statistic means that the observed data fits your expected data very well, denoting there is a correlation, respectively. The formula for calculating chi-square is as...