Data621 Homework 2
Eunkyu Hahm
10/14/2020
Homework #2 Assignment Requirements
In this homework assignment, you will work through various classification metrics. You will be asked to create functions in R to carry out the various calculations. You will also investigate some functions in packages that will let you obtain the equivalent results. Finally, you will create graphical output that also can be used to evaluate the output of classification models, such as binary logistic regression.
library(tidyverse)## ── Attaching packages ──────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.2.1 ✔ purrr 0.3.2
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## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(zoo)## Warning: package 'zoo' was built under R version 3.6.2##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
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## as.Date, as.Date.numericlibrary(knitr)
library(caret)## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
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## liftlibrary(pROC)## Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following objects are masked from 'package:stats':
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## cov, smooth, var1. Download the classification output data set (attached in Blackboard to the assignment).
data <- read.csv("https://raw.githubusercontent.com/ekhahm/datascience/master/classification-output-data.csv")
head(data)## pregnant glucose diastolic skinfold insulin bmi pedigree age class
## 1 7 124 70 33 215 25.5 0.161 37 0
## 2 2 122 76 27 200 35.9 0.483 26 0
## 3 3 107 62 13 48 22.9 0.678 23 1
## 4 1 91 64 24 0 29.2 0.192 21 0
## 5 4 83 86 19 0 29.3 0.317 34 0
## 6 1 100 74 12 46 19.5 0.149 28 0
## scored.class scored.probability
## 1 0 0.32845226
## 2 0 0.27319044
## 3 0 0.10966039
## 4 0 0.05599835
## 5 0 0.10049072
## 6 0 0.055154602. The data set has three key columns we will use: class, scored.class, scored.probability
data %>%
select(scored.class, class)%>%
table()## class
## scored.class 0 1
## 0 119 30
## 1 5 273. Write a function that takes the data set as a dataframe, with actual and predicted classifications identified, and returns the accuracy of the predictions.
Accuracy =\(\frac{TP+TN}{TP+FP+TN+FN}\)
accuracy <- function(x){
TP <- sum(x$class == 1 & x$scored.class == 1)
TN <- sum(x$class == 0 & x$scored.class == 0)
round((TP + TN)/nrow(x), 4)
}
accuracy(data)## [1] 0.80664. Write a function that takes the data set as a dataframe, with actual and predicted classifications identified, and returns the classification error rate of the predictions.
Classification Error Rate =\(\frac{FP+FN}{TP+FP+TN+FN}\)
class_er <- function(x){
FP <- sum(x$class == 0 & x$scored.class == 1)
FN <- sum(x$class == 1 & x$scored.class == 0)
round((FP + FN)/nrow(x), 4)
}
class_er(data)## [1] 0.19345. Write a function that takes the data set as a dataframe, with actual and predicted classifications identified, and returns the precision of the predictions.
Precision =\(\frac{TP}{TP+FP}\)
precision <- function(x){
TP <- sum(x$class == 1 & x$scored.class == 1)
FP <- sum(x$class == 0 & x$scored.class == 1)
round(TP/(TP + FP), 4)
}
precision(data)## [1] 0.84386. Write a function that takes the data set as a dataframe, with actual and predicted classifications identified, and returns the sensitivity of the predictions. Sensitivity is also known as recall.
Sensitivity =\(\frac{TP}{TP+FN}\)
sensitivity <- function(x){
TP <- sum(x$class == 1 & x$scored.class == 1)
FN <- sum(x$class == 1 & x$scored.class == 0)
round(TP/(TP + FN), 4)
}
sensitivity(data)## [1] 0.47377. Write a function that takes the data set as a dataframe, with actual and predicted classifications identified, and returns the specificity of the predictions.
Specificity =\(\frac{TN}{TN+FP}\)
specificity <- function(x){
TN <- sum(x$class == 0 & x$scored.class == 0)
FP <- sum(x$class == 0 & x$scored.class == 1)
round(TN/(TN + FP), 4)
}
specificity(data)## [1] 0.95978. Write a function that takes the data set as a dataframe, with actual and predicted classifications identified, and returns the F1 score of the predictions.
F1 Score =\(\frac{2 * Precision * sensitivity}{Precision + sensitivity}\)
f1_score <- function(x){
(2*precision(x)*sensitivity(x))/(precision(x)+sensitivity(x))
}
f1_score(data)## [1] 0.60676759. Before we move on, let’s consider a question that was asked: What are the bounds on the F1 score? Show that the F1 score will always be between 0 and 1. (Hint: If 0 < 𝑎 < 1 and 0 < 𝑏 < 1 then 𝑎𝑏 < 𝑎.)
0 < Precision < 1 and 0 < Sensitivity < 1. Therefore, F1 score is between 0 and 1
10. Write a function that generates an ROC curve from a data set with a true classification column (class in our example) and a probability column (scored.probability in our example). Your function should return a list that includes the plot of the ROC curve and a vector that contains the calculated area under the curve (AUC). Note that I recommend using a sequence of thresholds ranging from 0 to 1 at 0.01 intervals.
ROC <- function(x, y){
x <- x[order(y, decreasing = TRUE)]
TPR <- cumsum(x) / sum(x)
FPR <- cumsum(!x) / sum(!x)
xy <- data.frame(TPR, FPR, x)
FPR_df <- c(diff(xy$FPR), 0)
TPR_df <- c(diff(xy$TPR), 0)
AUC <- round(sum(xy$TPR * FPR_df) + sum(TPR_df * FPR_df)/2, 4)
plot(xy$FPR, xy$TPR, type = "l",
main = "ROC Curve",
xlab = "False Postivie Rate",
ylab = "True Positive Rate")
abline(a = 0, b = 1)
legend(.6, .4, AUC, title = "AUC")
}
ROC(data$class,data$scored.probability)
11. Use your created R functions and the provided classification output data set to produce all of the classification metrics discussed above.
metrics <- c(accuracy(data), class_er(data), precision(data), sensitivity(data), specificity(data), f1_score(data))
names(metrics) <- c("Accuracy", "Classification Error Rate", "Precision", "Sensitivity", "Specificity", "F1 Score")
metrics## Accuracy Classification Error Rate
## 0.8066000 0.1934000
## Precision Sensitivity
## 0.8438000 0.4737000
## Specificity F1 Score
## 0.9597000 0.606767512. Investigate the caret package. In particular, consider the functions confusionMatrix, sensitivity, and specificity. Apply the functions to the data set. How do the results compare with your own functions?
b <- data %>%
select(scored.class, class) %>%
mutate(scored.class = as.factor(scored.class),
class = as.factor(class))
c <- confusionMatrix(b$scored.class, b$class, positive = "1")
c## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 119 30
## 1 5 27
##
## Accuracy : 0.8066
## 95% CI : (0.7415, 0.8615)
## No Information Rate : 0.6851
## P-Value [Acc > NIR] : 0.0001712
##
## Kappa : 0.4916
##
## Mcnemar's Test P-Value : 4.976e-05
##
## Sensitivity : 0.4737
## Specificity : 0.9597
## Pos Pred Value : 0.8438
## Neg Pred Value : 0.7987
## Prevalence : 0.3149
## Detection Rate : 0.1492
## Detection Prevalence : 0.1768
## Balanced Accuracy : 0.7167
##
## 'Positive' Class : 1
## 13. Investigate the pROC package. Use it to generate an ROC curve for the data set. How do the results compare with your own functions?
par(mfrow = c(1, 2))
plot(roc(data$class, data$scored.probability), print.auc = TRUE)## Setting levels: control = 0, case = 1## Setting direction: controls < casesROC(data$class,data$scored.probability)