The liver
package
contains a collection of helper functions that make various techniques
from data science more user-friendly for non-experts.
Here is an example to show how to use the functionality of the package by using the churn dataset which is available in the package.
data( churn )
str( churn )
'data.frame': 5000 obs. of 20 variables:
$ state : Factor w/ 51 levels "AK","AL","AR",..: 17 36 32 36 37 2 20 25 19 50 ...
$ area.code : Factor w/ 3 levels "area_code_408",..: 2 2 2 1 2 3 3 2 1 2 ...
$ account.length: int 128 107 137 84 75 118 121 147 117 141 ...
$ voice.plan : Factor w/ 2 levels "yes","no": 1 1 2 2 2 2 1 2 2 1 ...
$ voice.messages: int 25 26 0 0 0 0 24 0 0 37 ...
$ intl.plan : Factor w/ 2 levels "yes","no": 2 2 2 1 1 1 2 1 2 1 ...
$ intl.mins : num 10 13.7 12.2 6.6 10.1 6.3 7.5 7.1 8.7 11.2 ...
$ intl.calls : int 3 3 5 7 3 6 7 6 4 5 ...
$ intl.charge : num 2.7 3.7 3.29 1.78 2.73 1.7 2.03 1.92 2.35 3.02 ...
$ day.mins : num 265 162 243 299 167 ...
$ day.calls : int 110 123 114 71 113 98 88 79 97 84 ...
$ day.charge : num 45.1 27.5 41.4 50.9 28.3 ...
$ eve.mins : num 197.4 195.5 121.2 61.9 148.3 ...
$ eve.calls : int 99 103 110 88 122 101 108 94 80 111 ...
$ eve.charge : num 16.78 16.62 10.3 5.26 12.61 ...
$ night.mins : num 245 254 163 197 187 ...
$ night.calls : int 91 103 104 89 121 118 118 96 90 97 ...
$ night.charge : num 11.01 11.45 7.32 8.86 8.41 ...
$ customer.calls: int 1 1 0 2 3 0 3 0 1 0 ...
$ churn : Factor w/ 2 levels "yes","no": 2 2 2 2 2 2 2 2 2 2 ...
It shows that the ‘churn’ dataset as a data.frame
has 20
variables and 5000 observations.
We partition the churn dataset randomly into two groups:
train set (80%) and test set (20%). Here, we use the
partition
function from the liver package:
The churn dataset has 19 predictors along with the target
variable churn
. Here we use the following predictors:
account.length
, voice.plan
,
voice.messages
, intl.plan
,
intl.mins
, day.mins
, eve.mins
,
night.mins
, and customer.calls
.
First, based on the above predictors, find the k-nearest neighbor for the test set, based on the training dataset, for the k = 8 as follows
formula = churn ~ account.length + voice.plan + voice.messages + intl.plan + intl.mins +
day.mins + eve.mins + night.mins + customer.calls
predict_knn = kNN( formula, train = train_set, test = test_set, k = 8 )
To report Confusion Matrix:
conf.mat( predict_knn, actual_test )
Actual
Predict yes no
yes 40 11
no 88 861
conf.mat.plot( predict_knn, actual_test )
To report Mean Squared Error (MSE):
The predictors that we used in the previous part, do not have the
same scale. For example, variable day.mins
change between 0
and 351.5, whereas variable voice.plan
is binary. In this
case, the values of variable day.mins
will overwhelm the
contribution of voice.plan
. To avoid this situation we use
normalization. So, we use min-max normalization and transfer the
predictors as follows:
To report Confusion Matrix:
To report the ROC curve, we need the probability of our classification prediction. We can have it by using:
prob_knn = kNN( formula, train = train_set, test = test_set, k = 8, type = "prob" )[ , 1 ]
prob_knn_trans = kNN( formula, train = train_set, test = test_set, transform = "minmax", k = 8, type = "prob" )[ , 1 ]
To visualize the model performance between the raw data and the
transformed data, we could report the ROC curve plot as well as AUC
(Area Under the Curve) by using the plot.roc
function from
the pROC package:
roc_knn = roc( actual_test, prob_knn )
roc_knn_trans = roc( actual_test, prob_knn_trans )
ggroc( list( roc_knn, roc_knn_trans ), size = 0.8 ) +
theme_minimal() + ggtitle( "ROC plots with AUC") +
scale_color_manual( values = c( "red", "blue" ),
labels = c( paste( "AUC=", round( auc( roc_knn ), 3 ), "; Raw data; " ),
paste( "AUC=", round( auc( roc_knn_trans ), 3 ), "; Transformed data" ) ) ) +
theme( legend.title = element_blank() ) +
theme( legend.position = c( .7, .3 ), text = element_text( size = 17 ) ) +
geom_segment( aes( x = 1, xend = 0, y = 0, yend = 1 ), color = "grey", linetype = "dashed" )
Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
3.5.0.
ℹ Please use the `legend.position.inside` argument of `theme()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
To find out the optimal value of k
based on Error
Rate, for the different values of k from 1 to 30, we run the
k-nearest neighbor for the test set and compute the Error Rate
for these models, by running kNN.plot()
command
kNN.plot( formula, train = train_set, test = test_set, transform = "minmax",
k.max = 30, set.seed = 3 )
The plot shows that the minimum value of Error Rate is for the case that k is 13; the smaller values of Error Rate indicates better predictions.