1. Common Linear & Classification for Machine Learning using Microsoft R
Venus Lin (Xiuqing Lin)
@azssugsqlpass
Assistant: Fred Benardella

2. Content iris Example Opening talks Machine learning RevoScaleR Package
linear and classification
iris Example
Linear model
Prediction
Visualization: what can we do in SSRS
Decision Tree and Clustering quick demo
Q & A

3. Yearn for the sea

4. Machine Learning What do you do with unlimited data?
What is the right question to ask about this data?
Accuracy of prediction
Human Understandable Formats
Structured and unstructured data (text, image, & media)
Netflix recommendation system contest award $1M to the algorithm 10% better than the Cinematch in 2009

5. RevoScaleR Package for Microsoft R
Scalable, Distributed and Parallel Computation,
Available along with Microsoft R Server and in-Database R Services.
With Prefix – rx
Name
Description
rxLinMod
Linear regression
rxDtree
Decision tree
rxKmeans
Classification

6. More about RevoScaleR

7. Linear Regression Name In RevoScaleR Package In Basci R Description
rxLinMod lm
Linear regression
rxDtree
rpart
Decision tree
rxKmeans
kmeans
Cluster

8. Work Flow Develop in R IDE – RStudio
Create a store procedure in SQL Server 2016
Visualize in SSRS

9. Iris – Data Frame Study

10. A Normal Analytics Project Process
Problem definition
Understand Iris species petal and sepal
Data exploration
Scatterplot & summary statistics
Data preparation
(N/A)
Modelling
Linear regression
Validation of data
Prediction
Implementation and tracking

11. Iris – Data Frame Study

12. Plot(iris)

13. iris[,3] >>> Petal.Length iris[,4] >>> Petal.Width
plot(iris[,4],iris[,3],type="p",pch=16,cex=1,xlab="Petal.Width",ylab="Petal.Length",main="Iris",col=iris$Species)

14. Linear Regression -Some basic statistic elements Dependent variable
iris[,3] >>> Petal.Length
Independent variable (predictor)
iris[,4] >>> Petal.Width
Formula
Petal.Length = a + b*Petal.Width

15. - How well does the model fit the data?
PPModel1<-rxLinMod(Petal.Length~Petal.Width,data=iris,covCoef = T)
PPModel1
summary(PPModel1)
names(PPModel1)
- How well does the model fit the data?
R Squared : percentage of the variances explained by the model
Significance of the models (P Value)
AIC / VIF / ANOVA / Residual Plot

16. Linear Regression - rxLinMod

17. Petal.Length = a + b*Petal.Width
Petal.Length = *Petal.Width
plot(iris[,4],iris[,3],type="p",pch=16,cex=1,xlab="Petal.Width",y lab="Petal.Length",main="Iris",col=iris$Species)
abline(PPModel1,lwd=5,col="red")

18. Prediction > new1<-data.frame(iris[,3:4])
> ppModel1_Predict<-rxPredict(PPModel1,data=new1,outData = new1,computeResiduals = T,interval = "prediction",writeModelVars = T,computeStdErrors = T)
> names(ppModel1_Predict)
[1] "Petal.Length" "Petal.Width" "Petal.Length_Pred"
[4] "Petal.Length_StdErr" "Petal.Length_Lower" "Petal.Length_Upper"
[7] "Petal.Length_Resid"
> plot(iris[,4],iris[,3],type="p",pch=16,cex=1,xlab="Petal.Width",ylab="Petal.Length",main="Iris",col=iris$Species)
> abline(PPModel1,lwd=5,col="red")
> points(ppModel1_Predict[,2],ppModel1_Predict[,5],col="green",type="l",lwd="4")
> points(ppModel1_Predict[,2],ppModel1_Predict[,6],col="green",type="l",lwd="4")

19. Create a stored procedure in SQL Sever Management Studio

20. SSRS Report

21. Name
In RevoScaleR Package
In Basci R
Description
rxLinMod lm
Linear regression
rxDtree
rpart
Decision tree
rxKmeans
kmeans
Cluster

22. Common Classification Model
rxDTree { from RevoScaleR Package}
Fit classification and regression trees
Demo
library(RevoScaleR)
library(RevoTreeView)
IrisTreeexample<-rxDTree(Species~Sepal.Length + Sepal.Width +Petal.Length +Petal.Width,data = iris)
plot(createTreeView(IrisTreeexample))

23. Name
In RevoScaleR Package
In Basci R
Description
rxLinMod lm
Linear regression
rxDtree
rpart
Decision tree
rxKmeans
kmeans
Cluster

24. K means clustering
iris_rxKmeans_3<-rxKmeans(formula = ~Petal.Length+Petal.Width,data=iris,numClusters = 3,outFile = "iriscluster.xdf",outColName = "Cluster")
iris_rxKmeans_3$cluster<-as.factor(iris_rxKmeans_3$cluster)
library(ggplot2)
ggplot(iris, aes(Petal.Width, Petal.Length, color = iris_rxKmeans_3$cluster)) + geom_point(size=5)+ggtitle("after K Means 3 cluster")

26. Plot(iris) – Sepal.Length /Width

27. iris_rxKmeans<-rxKmeans(formula = ~Sepal. Length+Sepal
iris_rxKmeans<-rxKmeans(formula = ~Sepal.Length+Sepal.Width,data=iris,numClusters = 3,outFile = "iriscluster1.xdf",outColName = "Cluster")
iris_rxKmeans$cluster<-as.factor(iris_rxKmeans$cluster)
library(ggplot2)
ggplot(iris, aes(Sepal.Length, Sepal.Width, color = iris_rxKmeans$cluster)) + geom_point(size=5)+ggtitle("after K Means 3 cluster")

29. numClusters = 5 Github: tomaztk/Compare_kmeans_rxKmeans
Is it possible to use RevoScaleR package in Power BI?
-From

30. Thank you Sponsors!

31. Thank you!