Supervised Machine Learning: Decision Trees and Random Forests

Videos

Topic	Access
StatQuest: Decision Trees Construct trees with GINI Impurity for Ratio interval Ordinal Nominal Level Data No flaws detected (17 min)	URL:https://www.youtube.com/watch?v=7VeUPuFGJHk
StatQuest: Decision Trees, Part 2 – Feature Selection and Missing Data (5 min)	https://www.youtube.com/watch?v=wpNl-JwwplA
Decision Tree 1: how it works (9 min) assumes that the tree end with pure sets !!! Entropy discussed in a shallow way	~~https://www.youtube.com/watch?v=eKD5gxPPeY0~~
Look at entropy from Thermodynamics point of view: https://www.grc.nasa.gov/www/k-12/airplane/entropy.html https://en.wikipedia.org/wiki/Entropy_in_thermodynamics_and_information_theory https://en.wikipedia.org/wiki/Entropy_(information_theory) https://towardsdatascience.com/entropy-is-a-measure-of-uncertainty-e2c000301c2c	Play List:https://www.youtube.com/watch?v=eKD5gxPPeY0&list=PLBv09BD7ez_4temBw7vLA19p3tdQH6FYO&index=1
A Gentle Introduction to Information Entropy	URL: https://machinelearningmastery.com/what-is-information-entropy/
Decision Tree (CART) – Machine Learning Fun and Easy (9 min)	https://www.youtube.com/watch?v=DCZ3tsQIoGU
Decision Tree In R (46 min) by Simplilearn last 15 minutes not useful for students	https://www.youtube.com/watch?v=HmEPCEXn-ZM
Pruning Classification trees using cv.tree ( 15 min)	https://www.youtube.com/watch?v=GOJN9SKl_OE
Machine Learning and Decision Trees ( first 15 minutes )	https://www.youtube.com/watch?v=RmajweUFKvM
Random Forest (27 min)	https://www.youtube.com/watch?v=HeTT73WxKIc

Articles

Medium	Topic	Access
Tutorial	Classification Trees	https://daviddalpiaz.github.io/r4sl/trees.html
Tutorial	A Complete Guide on Decision Tree Algorithm	https://www.edureka.co/blog/decision-tree-algorithm/
Tutorial	Decision Tree: How To Create A Perfect Decision Tree?	https://www.edureka.co/blog/decision-trees/
Tutorial	What is Overfitting In Machine Learning And How To Avoid It?	https://www.edureka.co/blog/overfitting-in-machine-learning/
Tutorial	Decision Tree in R with Example	https://www.guru99.com/r-decision-trees.html
Tutorial	Decision Tree and Random Forests	https://www.analyticsvidhya.com/blog/2016/02/complete-tutorial-learn-data-science-scratch/
Tutorial	Classification & Regression Trees	http://www.di.fc.ul.pt/~jpn/r/tree/tree.html

`rattle::fancyRpartPlot() is good`

https://www.gormanalysis.com/blog/magic-behind-constructing-a-decision-tree/

library(rattle)
library(rpart.plot)
library(RColorBrewer)

library(rpart)
mytree <- rpart(
  Fraud ~ RearEnd, 
  data = train, 
  method = "class"
)

# plot mytree 
rattle::fancyRpartPlot(mytree, caption = NULL)

library(rpart)

# Load the iris data that we've been working with
data(iris)

# Set the random number seed so that the results
# are the same each time we run the example (if we
# didn't set this the results might differ a bit each time
# because the subsampling process below is stochastic)
set.seed(2568)

Next, we will split our data into training data that we use to fit the model, and test data we use to validate the model. To do this, we will randomly select rows from our dataframe and assign them to the training data. All unsampled rows will be assigned to the test data set.

# Define a variable to subsample rows of the
# iris dataframe
n <- nrow(iris)

# Define a random subsample of the original data.
# We will use these data as a training data set,
# that we will then use to fit the model. The object
# `train` is just a random sample of row numbers from
# the total number of rows ini the iris data.
# Here, we take half the data
train <- sort(sample(1:n, floor(n / 2)))

# We can define separate data frames for the training
# data and the test data using the indices contained
# in `train`.

# Training data
iris.train <- iris[train, ]

# Test data
iris.test <- iris[-train, ]

Then, we can fit the classification tree to the training data.

# Fit the tree
iris_cart <- rpart(Species ~ ., # Formula: '.' means entire dataframe
  data = iris, # Using iris data
  subset = train # More specifically, the training data
)

By default, rpart uses gini impurity to select splits when performing classification. (If you’re unfamiliar read this article.) You can use information gain instead by specifying it in the parms parameter.

 




mytree <- rpart(
  Fraud ~ RearEnd, 
  data = train, 
  method = "class",
  parms = list(split = 'information'), 
  minsplit = 2, 
  minbucket = 1
)

https://www.gormanalysis.com/blog/decision-trees-in-r-using-rpart/


# Install and load the packages library(rattle) # We'll use this one to make some more functional plots library(RColorBrewer) # We'll use this one for colors 

 # Now, make it fancy 
fancyRpartPlot(iris_cart, main = "", sub = "")