Classification using Support Vector Machines

Abstract

We will look at the basic models for Classification of Data

Introduction

penguins %>% gf_point(body_mass_g ~ flipper_length_mm, colour = ~species)

library(p5)
library(tibble)

# Create drawings from data frames

squares <- data_frame(
  x = c(100, 100, 200, 200),
  y = c(50, 150, 50, 150),
  w = rep(40, 4),
  h = rep(40, 4)
)

squares %>%
  p5() %>%
  createCanvas(300, 200) %>%
  background("#002d72") %>%
  rect()

# Draw complex shapes

p5() %>%
  createCanvas(200, 200) %>%
  background("#DCDCDC") %>%
  arc(50, 55, 50, 50, 0, ~HALF_PI) %>%
  noFill() %>%
  arc(50, 55, 60, 60, ~HALF_PI, ~PI) %>%
  arc(50, 55, 70, 70, ~PI, ~ PI + QUARTER_PI) %>%
  arc(50, 55, 80, 80, ~ PI + QUARTER_PI, ~TWO_PI)

# Create a sketch piece-by-piece

squares %>%
  draw() %>%
  fill("#808080") %>%
  rect() %>%
  sketch(
    draw = .,
    setup = setup() %>% createCanvas(300, 200)
  )

# Create interactions

draw() %>%
  background("#F4F8FC") %>%
  line(~mouseX, 0, ~mouseX, 200) %>%
  sketch(
    draw = .,
    setup = setup() %>% createCanvas(300, 200)
  )

# Let users draw

p5() %>%
  createCanvas(400, 300) %>%
  background("#F4F8FC") %>%
  ellipse(~mouseX, ~mouseY, 30, 30)

# Click to change the brush

setup_ <- setup() %>%
  createCanvas(640, 380, ~WEBGL)
draw_ <- draw() %>%
  js("
      if (mouseIsPressed) {
        fill(0);
      } else {
        fill(255);
      }
  ") %>%
  ellipse(~mouseX, ~mouseY, 80, 80)

sketch(setup = setup_, draw = draw_)

What is Classification, again?

Classification is a Machine Learning method that predicts the class or level of a target Qualitative variable, based on several Qualitative and Quantitative predictors.

How does one Classify?

What is a Hyperplane?

Consider a dataset that contains a pair of Quantitative predictor variables and a target Qualitative variable.

ABCDEFGHIJ0123456789

x1 <dbl>	x2 <dbl>	y <fct>
18.381315	18.176596	-1
18.804433	3.635493	-1
6.436651	19.788943	1
16.778505	18.986696	1
13.193165	2.566314	-1
10.862823	10.770024	-1
14.995178	8.413866	-1
3.558665	18.209024	1
13.482854	9.492423	-1
14.396231	16.884081	1

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Figure 1: Hyperplane

References

1. https://towardsdatascience.com/data-science-made-easy-data-modeling-and-prediction-using-orange-f451f17061fa
2. The beauty of Random Forests: https://orangedatamining.com/blog/2016/12/22/the-beauty-of-random-forest/
3. Pythagorean Trees for Random Forests: https://orangedatamining.com/blog/2016/07/29/pythagorean-trees-and-forests/
4. data.tree sample applications, Christoph Glur, 2020-07-31. https://cran.r-project.org/web/packages/data.tree/vignettes/applications.html
5. https://ryjohnson09.netlify.app/post/caret-and-tidymodels/