I am using R programming language
Observations: 32,561Variables: 16$ age <int> 39, 50, 38, 53, 28, 37, 49, 52, 31, 42, 37, 30, 23…$ workclass <fct> State-gov, Self-emp-not-inc, Private, Private,…$ fnlwgt <int> 77516, 83311, 215646, 234721, 338409, 284582, 1601…$ education <fct> Bachelors, Bachelors, HS-grad, 11th, Bachelor…$ education.num <int> 13, 13, 9, 7, 13, 14, 5, 9, 14, 13, 10, 13, 13, 12…$ marital.status <fct> Never-married, Married-civ-spouse, Divorced, M…$ occupation <fct> Adm-clerical, Exec-managerial, Handlers-cleaner…$ relationship <fct> Not-in-family, Husband, Not-in-family, Husband…$ race <fct> White, White, White, Black, Black, White, B…$ sex <fct> Male, Male, Male, Male, Female, Female, Fem…$ capital.gain <int> 2174, 0, 0, 0, 0, 0, 0, 0, 14084, 5178, 0, 0, 0, 0…$ capital.loss <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…$ hours.per.week <int> 40, 13, 40, 40, 40, 40, 16, 45, 50, 40, 80, 40, 30…$ native.country <fct> United-States, United-States, United-States, U…$ income <fct> <=50K, <=50K, <=50K, <=50K, <=50K, <=50K, <…$ income_ind <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0,…income <=50K >50K 75.82485 24.17515
Use `KNN` algorithm to classify the earners (`<=50K,>50K`, low/high) for High-earners in the 1994 United StatesCensus data above. Select only the quantitative variables`age,education.num, capital.gain, capital.loss, hours.per.week`.Use `cl=train$income` and `k=1`(use the closest neighbor) in the`knn` argumnts. Print the confusion matrix. State the accuracy.
YOUR ANSWER HERE
“`{r} library(class)
# create training set only for select variables. Notice dplyr::before select. It is used to avoid conflict with
train_q <- train %>% dplyr::select(age,education.num,capital.gain, capital.loss, hours.per.week)
1. define knn classfier
2. look at the confusion matrix for the test set
3. print the confusion matrix
4. find the Accuracy = (true positive and truenegative)/total
“`
Expert Answer
Answer to I am using R programming language Observations: 32,561 Variables: 16 $ age 39, 50, 38, 53, 28, 37, 49, 52, 31, 42, 37, 3…