Machine Learning

{what do we do to data?}

{nothing}

Above all else, show the data

{clean}

{model}

{predict}

Machine learning and statistics are a set of tools used to ask questions about data.  They leverage mathematical concepts and computational abilities to make inferences about relationships, or make predications about unobserved contexts.

In general, you are faced with a tradeoff between:

Statistics

machine learning

Machine learning

statistics

source

Some other thoughts

Today we'll be performing a common machine learning task: classification.  

outlook

temp.

humidity

windy

skip class

Let's say I want to predict if a student will come to class...

outlook

temp.

humidity

windy

skip class

Let's say I want to predict if a student will come to class...

outcome

outlook

temp.

humidity

windy

skip class

Let's say I want to predict if a student will come to class...

outcome

attributes or features

outlook

temp.

humidity

windy

skip class

Write 3 rules to classify observations as skipping/attending class 

(if FEATURE(s) is VALUE, OUTCOME is VALUE)

outcome

attributes or features

this is a bit cumbersome....

this is awesome!

but how do we do it?

explained visually

Classification in R

Wrapping those tasks in functions

# Function to compare values
assess_fit <- function(model, data = homes, outcome = 'in_sf') {
  predicted <- predict(model, data, type='class')
  accuracy <- length(which(data[,outcome] == predicted)) / length(predicted) * 100
  return(paste0(accuracy, '% accurate!'))
}

# Use rpart to fit a model: predict `in_sf` using all other variables
basic_fit <- rpart(in_sf ~ ., data = homes, method="class")

# How well did we do?
assess_fit(basic_fit)

# Get a perfect fit: increase complexity, allow small splits
perfect_fit <- rpart(in_sf ~ ., data = homes, method="class",
                 control=rpart.control(cp = 0, minsplit=2))

assess_fit(perfect_fit)

What about testing/training data?

# Testing/training data: 
sample_size <- floor(.25 * nrow(homes))
train_indicies <- sample(seq_len(nrow(homes)), size = sample_size)
training_data <- data[train_indicies,]
test_data <- data[-train_indicies,]

# Train on training data, test on testing data: basic fit
basic_fit <- rpart(in_sf ~ ., data = training_data, method="class")
assess_fit(basic_fit, data=test_data)     

Seeing the tree!

# Visualize the tree using the base graphics package
png('visuals/tree_structure.png', width=900, height=900)
plot(basic_fit)
text(basic_fit, use.n = TRUE)
dev.off()

# Visualize basic fit
png('visuals/basic_fit.png', width=900, height=900)
fancyRpartPlot(basic_fit)
dev.off()

{could we build an interactive machine learning interface with Shiny....?}

Parameterizing the function

simple_tree <- function(predictors) {
  # Write out forumula
  predictors <- paste( predictors, collapse = "+")
  print(predictors)
  formula <- as.formula(paste0('in_sf ~ ', predictors))
  
  # Set test / training data
  sample_size <- floor(.25 * nrow(homes))
  train_indicies <- sample(seq_len(nrow(homes)), size = sample_size)
  training_data <- homes[train_indicies,]
  test_data <- homes[-train_indicies,]
  
  # Use rpart to fit a model: predict `in_sf` using other variables
  fit <- rpart(formula, data = training_data, method="class")
  
  # List of info to return
  info <- list()
  info$accuracy <- assess_fit(fit, data=test_data)
  p <- fancyRpartPlot(fit, sub='')
  info$tree <- p
  return(info)
}

server.R: reactive expressions

shinyServer(function(input, output){
  # Use a reactive expression so that you only run the code once
  getResults <- reactive ({
    return(simple_tree(input$features))
  })
  output$plot <- renderPlot({
    results <- getResults()
    return(results$plot)
  })
  output$accuracy <- renderText({
    results <- getResults()
    return(results$accuracy)
  })
})

ui.R

# Create UI
library(shiny)
shinyUI(fluidPage(
  # UI for the traffic simulation
  titlePanel('Housing Tree'),
  
  # Controls
  sidebarLayout(
    sidebarPanel(  
      checkboxGroupInput("features", label = h3("Features to Use"), 
           choices = colnames(homes)[2:ncol(homes)],
           selected = colnames(homes)[2:ncol(homes)])
    ),
    # Render plot
    mainPanel(
      plotOutput("plot"), 
      textOutput('accuracy')
    )
  )
))

{that's it!}

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