代做Classification留学生、代写ISLR package、代做Python，c/c++，Java编程

日期：2019-03-20 10:32

Homework2 - Classification

Mondy, Mar 11, 2019

Deadline: Mar 18, 2019

1. This question should be answered using the Weekly data set, which

is part of the ISLR package. This data contains 1,089 weekly returns

for 21 years, from the beginning of 1990 to the end of 2010.

For each date, we have recorded the percentage returns for each of

the five previous trading days, Lag1 through Lag5. We have also

recorded Volume (the number of shares traded on the previous day,

in billions), Today (the percentage return on the date in question)

and Direction (whether the market was Up or Down on this date).

library(ISLR)

data(Weekly)

a. Produce some numerical and graphical summaries of the Weekly

data. Do there appear to be any patterns? (scatter plots, correlation,

…)

b. Use the full data set to perform a logistic regression with Direction

as the response and the five lag variables plus Volume as predictors,

use stargazer to print the results. Do any of the predictors appear

to be statistically significant? If so, which ones?

c. Compute the confusion matrix and overall fraction of correct predictions.

Explain what the confusion matrix is telling you about the

types of mistakes made by logistic regression.

d. Now fit the logistic regression model using a training data period

from 1990 to 2008, with Lag2 as the only predictor. Compute the

confusion matrix and the overall fraction of correct predictions for

the held out data (that is, the data from 2009 and 2010).

e. Repeat (d) using LDA.

f. Repeat (d) using QDA.

g. Repeat (d) using KNN with K = 1.

h. Which of these methods appears to provide the best results on the

held out data?

i. Experiment with different combinations of predictors, including

possible transformations and interactions, for each of the methods.

Report the variables, method, and associated confusion matrix that

homework2 - classification 2

appears to provide the best results on the held out data. Note that

you should also experiment with values for K in the KNN classifier.

2. Using the Boston data set, in the library MASS, fit classification

models in order to predict whether a given suburb has a crime rate

above or below the median. Explore logistic regression, LDA, and

KNN models using various subsets of the predictors. Describe your

findings.