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

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

Yearn for the sea

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

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

More about RevoScaleR

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

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

Iris – Data Frame Study

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

Iris – Data Frame Study

Plot(iris)

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)

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

- 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

Linear Regression - rxLinMod

Petal.Length = a + b*Petal.Width Petal.Length = 1.08356 + 2.22994*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")

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")

Create a stored procedure in SQL Sever Management Studio

SSRS Report

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

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))

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

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")

Plot(iris) – Sepal.Length /Width

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")

numClusters = 5 Github: tomaztk/Compare_kmeans_rxKmeans Is it possible to use RevoScaleR package in Power BI? -From https://tomaztsql.wordpress.com/tag/revoscaler/

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