代写ISE529 Predictive Analytics 2024 Fall Homework 3代做留学生SQL 程序

日期：2024-11-01 10:10

ISE529 Predictive Analytics

2024 Fall

Homework 3

Due by: Oct. 13, 2024, 11:59 PM

1. (10 points)

In the QDA model, it is assumed that the predictors X within each class are drawn from a normal distribution with aclass-specific mean vector and a class-specific covariance matrix. We only consider the simple case where p = 1; i.e., there is only one predictor. Suppose that we have K classes, and that if an observation belongs to the kth class thenX comes from a one-dimensional normal distribution, X ~ N(µk, σk2). Show that in this case, the Bayes classifier is non-linear with quadratic form.

2. (15 points)

Suppose that you wish to classify an observation X ∈ R into apples and oranges. You fit a logistic regression model and find that

Your classmate fits a logistic regression model to the same data using the softmax formulation, and finds that

(a) What is the log odds of orange versus apple in your model?

(b) What is the log odds of orange versus apple in your classmate’smodel?

(c) Suppose that in your model, ˆβ0 = 2 and ˆβ1  = − 1 . What are the coefficient estimates in your classmate’s model? Be as specific as possible.

(d) Now suppose that you and your classmate fit the same two models on a different data set. This time, your classmate gets the coefficient estimates

What are the coefficient estimates in your model?

(e) Finally, suppose you apply both models from (d) to adata set with 2,000 test observations.

What fraction of the time do you expect the predicted class labels from your model to agree with those from your classmate’s model? Explain your answer.

3. (25 points)

Use Titanic data set (titanic_data.csv) to complete the following tasks:

(a) Fit a logistic regression model to the original data with Bernoulli response format. The response variable is “Survived”; the predictors include “Pclass”, “Sex”, and “Age” .

(b) Add anew column named as “Age_Rang” to the original data. Re-label the age of each individual according to 5-year interval bin [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80], then use “Pclass”, “Sex”, and “Age_Range” as predictors to fit a logistic regression model where “Survived” is the response variable. Check whether or not the logistic model is the same as the one in part (a)?

(c) Transform. the data set in part (b) to Binomial response format by grouping the columns “Pclass”, “Sex”, and “Age_Rang”. Fit a logistic regression to the grouped data. Check whether or not the logistic model is the same as the one in part (a) or (b)? (Hint: the response variables should include both “Survived” and “Died” columns).

(d) Are the logistic regression models in part (a), (b), and (c) adequate?

4. (15 points)

Entering high school students make program choices among general program, vocational program, and academic program. Their choice might be modeled using their writing score and  their social economic status, etc. The data set contains variables on 200 students. The response  variable is prog that is program type. The predictors include female, race, ses (social economic status), schtyp (school type), read, write, math, science, socst (social studies). The data can be found from highschooldata.csv. Split the dataset into two subsets, i.e., the first 190 observations as the training data, and the last 10 observations as the test data.

(a) Estimate a multinomial logistic regression model with the training data. (prog = 1, "academic" as the baseline model).

(b) Perform. prediction with the test data. Show the predicted probability as well as the program chosen.

5. (20 points)

Develop a model to predict whether a given car gets high or low gas mileage based on the Auto data set.

(a) Add a column mpg01 to the original data frame. The mpg01 is a binary variable that contains a 1 if mpg contains a value above its median, and a 0 if mpg contains a value below its median.

(b) Explore the data graphically with matrix of scatterplots and variance-covariance matrix to investigate the association between mpg01 and the other features. Choose three (3) the other features that seem most likely to be useful in predicting mpg01?

(c) Split the data into a training set with 85% of data points and a test set with 15% of data points.

(d) Perform. LDA, QDA, logistic regression, naive Bayes, and KNN, respectively, on the training data to predict mpg01 using the variables that seemed most associated with mpg01 in (b). What   is the test error of the model obtained? Which value of Kin KNN models seems to perform. the best on this data set?

6. (15 points)

The number of awards earned by students at one high school follows Poisson distribution. The “num_awards” is the response variable and indicates the number of awards earned by students at a high school in a year; “math” is a continuous predictor and represents students’ scores on their  math final exam, and “prog” is a categorical predictor with three levels indicating the type of program in which the students were enrolled (coded as 1 = “General”, 2 = “Academic” and 3 =    “Vocational”). The data can be found in Awards_data.csv. Split the dataset into two subsets, i.e., the first 190 observations as the training data, and the last 10 observations as the test data. (a) Estimate a Poisson regression model with the data set.

(b) Predict the number of awards earned by the students in a year with the test data. Show the test MSE.