Videos
Topic  Access 

StatQuest: Decision Trees Construct trees with GINI Impurity 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=wpNlJwwplA 
Decision Tree 1: how it works (9 min) assumes that the tree end with pure sets !!! Entropy discussed in a shallow way 

Look at entropy from Thermodynamics point of view: https://www.grc.nasa.gov/www/k12/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/entropyisameasureofuncertaintye2c000301c2c

Play List:https://www.youtube.com/watch?v=eKD5gxPPeY0&list=PLBv09BD7ez_4temBw7vLA19p3tdQH6FYO&index=1

A Gentle Introduction to Information Entropy  URL: https://machinelearningmastery.com/whatisinformationentropy/ 
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=HmEPCEXnZM 
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/decisiontreealgorithm/ 
Tutorial  Decision Tree: How To Create A Perfect Decision Tree?  https://www.edureka.co/blog/decisiontrees/ 
Tutorial  What is Overfitting In Machine Learning And How To Avoid It?  https://www.edureka.co/blog/overfittinginmachinelearning/ 
Tutorial  Decision Tree in R with Example  https://www.guru99.com/rdecisiontrees.html 
Tutorial  Decision Tree and Random Forests  https://www.analyticsvidhya.com/blog/2016/02/completetutoriallearndatasciencescratch/ 
Tutorial  Classification & Regression Trees  http://www.di.fc.ul.pt/~jpn/r/tree/tree.html 
rattle::fancyRpartPlot() is good
https://www.gormanalysis.com/blog/magicbehindconstructingadecisiontree/
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.

https://www.gormanalysis.com/blog/decisiontreesinrusingrpart/
# 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 = "")
in the tree below
the first node include 100% of observations in which setosa species is most frequent
A decision is made based on Petal.Length<2.6
Node 2 has only Setosas 29% of all observations and 100% of it are setosa
Node 3 is composed of two species with a 52% virginica, 61% of all observations are here
A decision is made based on Petal.Length<4.8
Node 6 has .31% of observation in which 96% are versicolor
Node 7 has 31% of observation in which 100% are virginca
also rpart.plot() is good
also Library(party) is good https://www.youtube.com/watch?v=GCXsKNMDy1w
This article explains finding the best cp pruning threshold
https://danstich.github.io/stich/classes/BIOL217/12_cart.html