Develop predictive models in R using packages like MASS, caret, randomForest, and forecast from regression to time series.

1. Model Automation in R
Using MASS, randomForest, forecast,
and caret

2. Who is Will Johnson?
● Database Manager at Uline (Pleasant Prairie)
● MS Predictive Analytics (2015)
● Operating www.LearnByMarketing.com
○ R tutorials, thoughts on analysis.
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3. Agenda
1. What is Model Automation
2. Pros and Cons of Model Automation
3. Decision Trees and Random Forests {randomForest}
4. Stepwise Regression {MASS}
5. Auto.Arima for time series {forecast}
6. Hyperparameter Search {caret}

4. What is Model Automation?
Hypothesis Space
vs
Hyperparameter Space

5. Pros and Cons of Model Automation
PROS:
● You Don’t Have to Think!
● “Faster” Iterations.
● See what’s “Important”
CONS:
● You Don’t Have to Think!
● Jellybeans

7. Agenda
1. What is Model Automation
2. Pros and Cons of Model Automation
3. Decision Trees and Random Forests {randomForest}
4. Stepwise Regression {MASS}
5. Auto.Arima for time series {forecast}
6. Hyperparameter Search {caret}

8. Decision Trees
● Gini Index +
Entropy

9. randomForest
● Mean Decrease
in Gini Index
library(randomForest)
rf <- randomForest(y~., data = dat)
rf$importance #Var Name + Importance
varImpPlot(rf) #Visualization

10. Stepwise
Regression
● AIC

11. Stepwise
Regression
library(MASS)
mod <- lm(hp~.,data=mt)
#Step Backward and remove one variable at a time
stepAIC(mod,direction = "backward",trace = T)
#Create a model using only the intercept
mod_lower = lm(hp~1,data=mt)
#Step Forward and add one variable at a time
stepAIC(mod_lower,direction = "forward",
scope=list(upper=upper_form,lower=~1))
#Step Forward or Backward each step starting with a intercept model
stepAIC(mod_lower,direction = "both",
scope=list(upper=upper_form,lower=~1))
#Get the Independent Variables
#(and exclude hp dependent variable)
indep_vars <-paste(names(mt)[-which(names(mt)=="hp")],
collapse="+")
#Turn those variable names into a formula
upper_form = formula(paste("~",indep_vars,collapse=""))
#~mpg + cyl + disp + drat + wt + qsec + vs + am + gear + carb

12. Auto.Arima
● Time Series models.
● AutoRegressive…
● Moving Averages…
● With Differencing!
library(forecast)
library(fpp)
#Step Backward and remove one variable at a time
data("elecequip")
ee <- elecequip[1:180]
model <- auto.arima(ee,stationary = T)
# ar1 ma1 ma2 ma3 intercept
#0.8428 -0.6571 -0.1753 0.6353 95.7265
#s.e. 0.0431 0.0537 0.0573 0.0561 3.2223
plot(forecast(model,h=10))
lines(x = 181:191, y= elecequip[181:191],
type = 'l', col = 'red')

13. Auto.Arima

14. train {caret}
library(caret)
#Step Backward and remove one variable at a time
tctrl <- trainControl(method = "cv",number=10,
repeats=10)
rpart_opts <- expand.grid(cp = seq(0.0,0.01, by = 0.001))
rpart_model <- train(y~. data, method="rpart",
metric = "Kappa", trControl = tctrl,
tuneGrid = rpart_opts, subset = train_log)

15. train {caret}

16. Recap
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library(randomForest) varImpPlot()
library(MASS) stepAIC()
library(forecast) auto.arima()
library(caret) train()

17. Questions?
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