data frames代写、代做R编程语言、代写R设计、UCI Machine代做

日期：2019-12-03 09:32

Assigned:

November 17, 2019 Homework 4

Due:

December 01, 2019

Please complete the assigned problems to the best of your abilities. Ensure

that the work you do is entirely your own, external resources are only used as

permitted by the instructor, and all allowed sources are given proper credit for

non-original content.

1 Recitation Exercises

These exercises are to be found in: Introduction to Statistical Learning,

1.1 Chapter 8

Exercises: 1,3,4,5

1.2 Chapter 10

Exercises: 1,2,3,4,6

2 Practicum Problems

These problems will primarily reference the lecture materials and the examples

given in class using R and CRAN. It is suggested that a RStudio session be

used for the programmatic components.

2.1 Problem 1

Simulate a binary classification dataset with a single feature via a mixture of

normal distributions using R (Hint: Generate two data frames with the random

number and a class label, and combine them together). The normal distribution

parameters (using the function rnorm) should be (5,2) and (-5,2) for the pair of

samples - you can determine an appropriate number of samples. Induce a binary

decision tree (using rpart), and obtain the threshold value for the feature in

the first split. How does this value compare to the empirical distribution of

the feature? How many nodes does this tree have? What is the entropy and

Gini at each? Repeat with normal distributions of (1,2) and (-1,2). How many

nodes does this tree have? Why? Prune this tree (using rpart.prune) with a

complexity parameter of 0.1. Describe the resulting tree.

2.2 Problem 2

Load the Wine sample dataset from the UCI Machine Learning Repository

(wine.data) into R using a dataframe (Note: The column names will need

to be loaded separately). Use either the prcomp or princomp methods to

perform a PCA of the wine data - justify whether scaling of the inputs should

Prof. Panchal:

Wed. 6:25PM-9:05PM

MATH 571 - Data Preparation & Analysis Fall 2019:

Section 01-03

Assigned:

November 17, 2019 Homework 4

Due:

December 01, 2019

be used or not when performing the decomposition. Plot a biplot of the results

- identify a feature which is pointed in the opposite direction of Hue in

principal component/rotated feature space. What does this imply regarding the

correlation of this feature to Hue? Support your result with a calcualted value.

Finally, plot a screeplot of your results and determine the percentage of total

variance explained by PC1 and PC2.

2.3 Problem 3

Load the USArrests sample dataset from the built-in datasets (data(USArrests))

into R using a dataframe (Note: Row names are states, not numerical values!).

Use the kmeans package to perform a clustering of the data with increasing

values of k from 2 to 10 - you will need to decide whether or not to center/scale

the observations - justify your choice. Plot the within-cluster sum of squares for

each value of k - what is the optimal number of clusters? Use the tidyverse and

fviz cluster plotting method from factoextra to plot the optimal clustering.

2.4 Problem 4

Load the Wine Quality sample dataset from the UCI Machine Learning Repository

(winequality-white.csv) into R using a dataframe (Note: There is both

a red and white wine file, we will use white!). Excluding the quality target

variable, use hclust to perform a hierarchical clustering of the data with single

as well as complete linkage. You will need to decide on whether or not

to center/scale the observations - justify your choice. At what distance value

are the two penultimate clusters merged? Use the cutree method to obtain

these two clusters, and calculate their summary statistics. What feature means

have the largest differences? Which linkage method produces are more balanced

clustering?

Prof. Panchal:

Wed. 6:25PM-9:05PM

MATH 571 - Data Preparation & Analysis Fall 2019:

Section 01-03